Are there any top 100 prospects who are known to have possible age fraud issues? This kind of thing is never talked about publicly until a guy is caught. Wondering how widespread the issue is today and if any teams have inside info. (vs. the entire league sharing the suspicions about the same players).

Any view on Tony Sanchez's season and odds of becoming a starting MLB catcher?

There's nothing to "get to the bottom of" from an analytical point of view. It doesn't matter how much you care or don't care about the issue; there is no way to intelligently adjust past performance for drug use because we don't know who used what when. Throwing your hands in the air and saying that all past stats are useless (as you imply in your PECOTA house of cards comment) is your option. The rest of us will continue to treat drug use as just another one of hundreds of variables that we can't measure accurately enough to include in a predictive model.

Is there any concern whatsoever about the legitimacy of Sano's age? Not that I have heard anything, but just asking if any teams feel that way. More generally, do some teams factor in an age "adjustment" if they suspect a player has a decent chance of using a fake age, or has this practice become so rare that it would be foolish to do so?

Absolutely. I might seem ridiculous, but I think there should be a monthly overall top 101 released during the season. It's easy to say player x has off to a good/bad start, but harder to estimate just how much his prospect stock has changed as a result.

Except once you have part of a crew doing the replay, his objectivity is in question. Anything remotely close, he will probably side with the umps' original call...and expect the same treatment when he on the field in 4 out of 5 games. Best solution is to have a non-umpire in the booth.

I choked on my coffee when I read this: "With his purse strings wound tight around the Steinbrenners' fingers in hopes of staying under a $189 million budget in 2014..."

And which is why if you're serious about pitching projections, you'll use Steamer instead of PECOTA. It at least attempts to factor in major velocity loss into its projections.

I hope you are joking. A player projection system does not try to project front office moves. If you are that certain you know what a front office will do, apply that knowledge to the raw projections yourself.

Disagree. I like to hear about the players whose performances have been the most unusual -- that is, those most likely to represent a change in prospect value -- whether it's an 18 year old or a 24 year old.

I called it a "horrific start." How is that putting "weight" on anything? If you have no information to add (and clearly you don't), that's totally fine....and why I posed the question to Zach, not you.

A 21/17 K/BB ratio for a top pitching prospect is horrific in my book.

If you ran this same analysis on fake hitting coaches and seasons (randomly assigning batters to groups of the same sizes), would the above tables all be expected show zeroes? I.e., what are the odds this is all noise?

Anything on Gerrit Cole or Mike Olt? Truly horrific starts of the season for each. Olt appears to still be out (but not on the DL). Kind of odd.

How does a subpar catcher throw out 26% of runners for his career? That's right around MLB average. Only 8 guys have attempted to run on him this season, so it seems like teams are reading a different scouting report than you are. P.S. I would agree with you that pitch-framing analysis is becoming a bit overexposed. Not sure we need weekly columns on it.

A big part of framing is making some kind of effort to hold the ball after the catch and allow the ump time to make a (presumably difficult) decision. Norris' "effort" is terrible (above); he's already started to throw the ball back to the pitcher before the ump even has a chance to make a strike call.

Any idea what the Nats' plan is for Rendon? It seems like if they want him to be a 2B long-term, they should be having him play 2B in the minors now...but he's not.

Couldn't disagree more. Hate the expanded rosters in September. It's a completely different and less entertaining game.

The smaller the number of RPs, the fewer advantageous LHP vs. LHB matchups you can engineer, and thus the higher your overall bullpen ERA.

For pitching, you'd be nuts to use PECOTA over Steamer. Major issues. For a random 2013 example, see: Marmol, Carlos.

Thanks, but honestly too late to help for fantasy purposes.

I'll certainly sell you that $3 Rivera-Chapman spread. Marmol actual: 6.5 BB/9 last 2 years. Marmol projected: 1.20 WHIP. Can't wait to hear what the auditors have to say about the PECOTA process next year..

Ben -- any chance you can post an article examining pitching velocity gainers/losers this spring?

Any way to dig up the code from Nate Silver's last year running PECOTA, and produce some provisional projections for this year? Even if they were flawed, they seemed to be fundamentally sound and offered an alternative view of the career paths of important players.

Yeah, that would be great.

Can someone at BP run a similar article for this pre-season?

Great article.

Also: http://www.hardballtimes.com/main/article/lose-a-tick-gain-a-tick/

I second that. Would be great to have a running list (updated monthly year-round) showing the top ~500 dynasty values (incl. college/HS/foreign prospects, minor leaguers, and MLBers).

aaaand, still waiting.

When are long-term forecasts coming out?

Just curious: did you have pitches per inning as available factor?

Couldn't you look at groundball rates instead?

What is the ETA for PECOTA long-term projections?

I agree. Are we to assume that a performance evaluation was done behind the scenes? Wyers said, "A full list of changes would be rather long and frankly a little tedious". I find that to be hard to believe (a few hours of labor would be well worth it). More generally, I cannot understand why PECOTA has become a blacker and blacker box since Silver left. Throw us a bone here. What improvements were made and why?

General prospect question: do teams quietly apply an age inflation factor to the listed ages of Latin American players? It seems like whenever an age has proven to be false in the past, articles were written suggesting that teams widely "knew" the player was a couple years older than listed, and that the info. had already been factored into the player's contract/trade value. The question is how widespread is lying about age believed to be today? If it is still significant, do teams have a decent feel for which players are higher risk or rather are they forced to hedge and apply a slight inflation factor across the board based on nationality?

I totally agree with your last paragraph. People are so eager to claim that outliers will not repeat that they miss the very real possibility that a certain human being might behave abnormally in certain situations due to a very real and repeatable cause. Josh Hamilton cannot possibly have persistent day/night splits bc most players do not. Jered Weaver is apparently no more likely than the average pitcher to outperform at home next year. Justin Upton's ridiculous career home/road splits: sheer noise. Luke Hochevar: chronically unlucky, mostly because Hellickson stole all his luck. The list goes on and on. One reason I am confident that people have gone off the deep end in applying generalizations to tail cases is that I have made a living betting on baseball for the past 10 years, and it's been in no small part due to taking the other side of these inaccurate predictions of mean reversion (provided the sample is large enough to refute it). I hope someone here or elsewhere can devote an entire series to examining serious career outliers (in FIP vs. ERA, handedness splits, etc.) apart from the rest of the population and determine if such players should be lumped in with the rest of the player universe, or rather if we should be applying different predictions of regression.

Never cared about the hall of fame; never will.

I know you are not necessarily suggesting something this extreme, but I just wanted to mention that the Orioles attempted to profile prospects based on an objective measure of makeup/drive about 10 years ago. Based on their propsects' (lack of) success from that era, it didn't work so well: "Since Ritterpusch returned to the fold, the Orioles have loaded up on prospects who rate five or five-plus on their scale. Most are pitchers: Adam Loewen, Kurt Ainsworth, Ryan Hannaman, Don Levinski, and Chris Ray are among them. Daniel Cabrera graded well on a Spanish version of the test that the Orioles recently developed, and his profile convinced the Orioles that he could mentally handle the callup to the majors from Double-A, according to Ritterpusch." http://www.oobleck.com/orioles/archives/2004/05/inside_the_orioles_heads_part.html

The odds that Tabata is really under 25 y.o. are very small...

Abnormal, 1-year home/road splits are not all predictive.

Turns out I was pretty dead on. How's Bleacher Report treating you?

Nice. Any chance of adding TAv to the table, and of allowing queries for multiple seasons (e.g., 2009-2011)? Thanks.

In order for this to be true, these aces must be underperforming, on average, in the playoffs. The first step would to show the effect explicitly and then if verified, speculate as to why the anomaly is occurring.

A couple issues: 1) There is no healthy "ace" on the Orioles. The starters are all pretty similarly bad. 2) Even assuming there was an ace a full 1.0 RA/9 better than than the next best pitcher, the math behind your theory doesn't work. Assume the following: By fully punting the game (starting AAA scrubs), you have a 10% chance of winning game 1 (of these 2 theoretical games). With your ace on the mound and normal batters vs. Sabathia, you have a 40% chance of winning game 1. If you play Oakland with your ace and an extra-rested team, you have 50% chance of winning game 2. With a tired team and your #2 pitcher vs. Oakland, you have a 40% chance of winning (8% delta due to SP downgrade of 1.00 RA9, and 2% due to less rest for team). So, punting game 1 means odds of advancing to ALDS = 0.1 + 0.9*0.5 = 55%. Starting ace in game 1 means odds of advancing = 0.4 + 0.6*0.4 = 64%. Not even close, and that is assuming you have a true ace. You can even make odds of beating Sabathia 30% (with your tired ace and team), and it still is not advantageous to punt a game. Game theory certainly applies when deciding whom to start in a particular game of a sequence, as Marchi has already pointed out (http://www.baseballprospectus.com/article.php?articleid=18263), but frankly that has always been true for postseason situations. The concept of not sending your best hitters in a given game would not make sense in any realistic situation in my opinion.

I think you are measuring the wrong thing here. People don't necessarily think you need an elite pitcher to outperform in the post-season; they think you need a group of top 3 starting pitchers that is significantly better than your overall starting pitching for the season.

No answers from BP? Do you guys not even read the comments?

I am no fan of the current iteration of PECOTA myself, but sbnirish77 is making no sense. Let's assume BP decides that is it going to assume that 30% of all players were using PEDs over the past 20 years. How would this change PECOTA? Should they shut down any effort to project players simply because not all variables are known?

I don't agree about it being reasonable at all. You're an elite athlete (in theory). You're getting paid tens of millions of dollars to be in peak physical and mental form. Would it kill you to lay off beer for six months a year? You have from age 40 till death to drink as much beer as you please. I happen to love beer, and drink it regularly. I would, however, like to think I have the shred of self-control needed to not drink it less than 24 hours before I was about to be paid ~$50,000 to perform athletically for three hours as well as I possibly could.

Name another 19-year-old with a .840 OPS at any position in the Carolina League... It's only happened once in the last 10 years (Wil Myers 2010). Either Bogaerts is much older than his listed age or he is an underrated prospect.

How exactly would a person not in your league use this info.? Presumably he already knows what the hometown bias is is in his own draft, so glancing at what that bias looked like in some random Phillies draft is not really going help.

Shhhh. Be quiet. You're not allowed to mention that the site is falling apart when BP's leaders are too busy picking internet fights.

You should know better than to comment on things of which you have no knowledge. I read the entire article, incl. the 75% of it devoted to strained historical references. I elucidated the point the article made quite clearly and refuted it. If you have something to add, go fot it. If not, you're better served lurking.

For people who find it painful to watch BP aimlessly try to debug a black box system for 16 months, I humbly suggest you google "clay davenport Postseason Odds". His methodology is not perfect IMO, but at least it is stable and logical.

What specific shortcomings in Colin's work which I pointed out are "ignoring facts and context"? I ask very specific important questions, and he ignored all them. Meanwhile, he took to Twitter to mock the entire exercise.

"If age is not a factor, then should we just tell 14-year olds to toss 110 pitches per game, with the hope that they have resilient enough ligaments/muscles to withstand such abuse? " This is completely misreading my comment. My comment says nothing about changing practices. It merely makes the observation that you have two possible explanatory variables, and your article is assuming that just one (age) is sufficient. Pitchers may be getting hurt in clusters around a certain age because: 1) There is something unique to that age that drives injury. or 2) The majority of pitchers of that age have just recently received sufficient lifetime workload to cause body parts to break down for the first time. My point is that there is nothing terribly special about the age; it's the cumulative arm use that matters. Once a player has shown that he can survive long-term use without injury (by whatever exact measure used and by whatever age this may occur), it's reasonable to guess that he has technique or physiology that is better than average. The only way to show that age alone is a significant factor would be to measure injury rates by age after controlling for lifetime workloads. I actually think that would be an interesting study that could yield useful fundings.

Is there anywhere on the site to find sortable rankings of catchers for these three metrics?

One likely explanation for this effect is individual game environmental effects. That is, higher than normal temp. and/or wind blowing out (relative to park avgs.) will lead to longer innings for both teams and poorer pitcher performance.

If only someone would try to explain PECOTA as lucidly as James did Win Shares, there might a chance people could understand, critique and try to improve it.

Any idea what the park factor was like for Koufax?

How much has Shelby Miller's velocity dropped this season?

Totally agree with your complaint about the need to catch strike three. Similarly (in my mind), why are runners apparently allowed to skip touching a base as long as the opponent doesn't "appeal" by throwing to the skipped base after the fact? I think a pretty simple rule change would be if a runner touches a base out of order (third after missing second, e.g.), he would be called out immediately.

I think it's also worth noting that all four of Scherzer's opponents in May (ChW twice, Pit and Oak) have ranked in the top 10 in team K%, whereas only one April opponent did.

It should probably be renamed to reflect it is a representation of the team's true strength, and is not the win pct. 'expected' to be seen going fwd. -- which is certainly what the current name implies.

"Under such circumstances, it is a miracle that Prior pitched at all in 2004." Um what? If PAP was so perfectly predictive of next season injury, don't you think we would have seen a massive drop in pitcher injuries over the past 10 years? Also, the "injury nexus" is, in my opinion, very likely to be a selection effect -- not an age-related effect. That is, certain physiologies can withstand chronic abuse better than others; naturally the differentiation won't show itself until sufficient abuse has been accumulated -- typically the pitcher's early 20s. The only ones who make in past that age intact are those with unusually resilient ligaments/muscles, etc. I think this drives most, if not all of the observed effect.

So, in conclusion, extreme outliers in mid-May may (or may not) revert to something closer to mediocre the rest of the season?

Retail through insurance may be billed at $2k, but certainly high-volume with no insurance involved is a lot closer to $500. Either way, it's a trivial cost compared to top pitcher salaries.

Incredibly rare.

Really? So if the MRI shows a torn UCL and there is no pain, you just assume the pitcher doesn't need a UCL?

Regarding #11, it's pretty clearly driven by the increase in avg. pitch speed (coupled with the lack of increase in hand-eye coordination of the batters).

If I am reading the article correctly, your "neutral park" in the last two tables is implicitly assumed to have MLB-average temperature (for a given time/date), right? If so, it's pretty surprising to the Rangers with the coldest "gun". Their avg. game temp. is 10-12 deg. higher than rest of MLB, which looks like it should be worth an additional ~0.25 mph. By the way, what would in theory cause a systemic miscalibration of the system at a given park? I don't know the exact technology, but I would think measuring velocity would be far less prone to calibration issues than measuring location. But maybe not.

Really impressive stuff. Can you post the park factors you have calculated? One comment: time of day and temp. should have a pretty sizeable correlation. Is this accounted for? Also, in some parks (e.g., Fenway), temp. is correlated with wind direction. To assess the true impact of temp., I would think you would have to evaluate that.

And among relievers with 10+ saves since 2010, a 3.15 xFIP ranks only 17th best. I guess I am not clear on the conclusion of the article. Is Walden a quality closer and improperly used, or is he just a decent reliever in your view?

It's not the blown saves. It's not the small sample. It's the 24 BB in 50 IP in the minors in 2010, the 26 BB in 60 IP last season, and the 4 BB in 4.1 IP this season. Unless you are sporting a 15 K/9 like Kimbrel, that's probably not going to cut it as a closer on a contending team.

Why don't teams perform routine monthly MRIs on all quality pitchers in the org.? It takes 30 min., is non-invasive and costs all of $500. Why wait until the pitcher complains of pain or shows decreased performance?

And what exactly makes you think that a whopping 62 career IP vs. left-handed batters is predictive of future split performance? Even if you ignore the research and assume his past splits are predictive, his xFIP (generally considered a better predictor than FIP for small samples) vs. LHB is 3.93, and it's 2.46 vs. RHB. Seems like the Giants might actually know a bit more about platoon splits than you do..

Because pitchers are throwing a ton harder than they used to. None of these workhorses from the 60s would even make it out of triple-A today. When you max out the human anatomy, parts will break. Technique, increased muscle size and increased frame size have all resulted on greater forces on body parts that one cannot always strengthen and develop equally to handle the added load.

TAv-against is not adjusted for league (presence of DH). So you cannot fairly compare pitchers from different leagues using this metric. E.g., pitching in Florida (no DH) will make Johnson's numbers appear better than his true skill.

Couldn't disagree more. As pointed out by numerous Amazon reviews, editorial quality went severely downhill under Goldman. Hopefully this will be addition by subtraction.

Good riddance, Goldman. The editorial quality of the BP annual went severely downhill under your leadership. The number of articles needlessly mentioning Yankees and/or political views skyrocketed. Subscribership plummeted. Addition by subtraction I suppose.

One goal of sabermetics is to understand basic statistical concepts, like selection bias. I'm stunned by this article.

http://adblockplus.org/en/ can work wonders in taming aggressive ads.

Where is Upside on the player cards? It's the day before Opening Day, and no sign of it thus far.

Is the projected record based on actual schedule, or a generic AL schedule?

Does this use actual team schedules or generic schedules?

This an incredibly long-winded article to say absolutely nothing. For anyone skipping to the comments, I can summarize the whole article as follows: --------- Matusz had a bad year last year, partly due to injury. He has really good peripheral stats this spring training, which apparently have no predictive value. Also, he got a couple "lucky" double plays and hit 2 batters. Also, apparently BABIP and HBP rates are predictive from one outing to the next. --------- So the author criticizes Matusz for BABIP, extra double plays and hitting 2 batters, while dismissing the exceptional K9 and BB9 rates he has had this spring? First of all, Mike Podhorzer recently published research showing that spring training K and BB rates are suprisingly correlated to regular season rate stats. So odds are Matusz spring contains at least some information. More generally, the author has zero handle on what is and is not predictive with regard to pitching. You don't need to manually compensate for "lucky" double plays if you are looking at the correct peripheral stats to begin with. I could go on, but not worth anyone's time. Truly shocked this aritcle made it on to BP.

ETA for 10-year projections?

I don't understand how you consider the fact that some teams have a bad player at a given position a "major inefficiency at work." Talent is never evenly distributed among all teams, nor would one expect it to be given the long-term nature of most contracts. The only way to show it is a true inefficiency is to prove that there are free agents or bench players teams are willing to give away who can hit much better than the bad LFers you profile, while not giving up the gains through worse defense -- unclear given that you only measured the defense of players who actually played a full season in LF. Without naming these replacement LFers, projecting their actual defense, and showing that they would cost nothing to acquire/promote, I'm not seeing any obvious action teams should take to close the supposed inefficiency. Also, I think any comparison of LF vs. RF defense needs to consider the number of fielding chances each position sees.

Apparently not available yet. No one at BP seems willing to commit to an ETA. (Last year, they were delayed until after the season had started).

Thanks. Would also be good if BP could add the option to input keeper (or otherwise unavailable) players in the main interface, independent of other settings. E.g., provide a link to "edit available player pool."

How do you mark players as taken? I do see any option to do so (I have read the Detailed Help section, and there is no mention of it).

roughly, are we talking days or weeks away?

ETA on the cards?

A cap on sentence length -- consider it... I would suggest some number under 98 (words): "I leave it to those who have studied the schedule more closely than I to argue that the lack of off-days in the modern calendar requires that teams carry more than ten pitchers lest they fatigue the lot, but unless confronted with incontrovertible evidence that the last man cannot be stashed at Triple-A until the appropriate moment and then used in a taxiing rotation with a few other similarly semi-talented unworthies, I refuse to believe that the baker's dozen pitching staff is anything more than a security blanket for wet managers, or a wet blanket for insecure managers. "

This article is so irrelevant to baseball. It just strikes me as comment fly paper. Honestly, Goldman, there have to be better ways to spend your baseball-directed time with opening day less than two months away.

Correction: I was looking at an old card for Kershaw. (The cards are apparently not updated yet).

Can you write up what methodology is used to generate peripheral stat projections? E.g., R, RBI, SB, W, L.

Did you take a look at very old and young players specifically? I would think that if a 39 year old player's stats are falling off a cliff over last 3 years, it would make sense to use a more aggressive aging curve than the curve used on an average 39 year old. Also, why not let the season sequence info. contribute to the comparable player-finding? Right now, you generate a single number (baseline forecast) using certain weights, and then try to find historical players with similar stats and other attributes. [Correct me if this is inaccurate]. I would suggest looking for historical players who have experienced season sequences similar to the player in question (say, .250,.290,.230) over the last x years, and see how they performed. x would certainly need to be greater than 3. It may not even be the sequencing that provides extra info.; it may the y2y volatility or lack thereof. And clearly the criteria for "similar" season sequences (a.k.a., "career paths") would need to be pretty loose to create a sufficient sample and not overfit. Is this type of approach (using more than 3 past seasons and using loosely-bucketed career paths to find comps) under consideration for future versions of PECOTA?

I think Boras should use PECOTA aging curves in his next negotiations. Old guys get older, but their performance apparently stays about the same. Torii Hunter will apparently be a league-average hitter well into his mid-40s. But it won't even be a market inefficiency for teams to exploit, as the entire league will be filled with 45-year-old .260 TAv hitters by 2018.

The ten-year forecasts look poorly smoothed at best, poorly conceived at worst. E.g., Kershaw's 10-year eqERA goes: 3.65 3.75 3.75 3.56 3.81 3.85 3.76 3.76 3.63 3.52 So he is still getting worse right now (age 23), but when he turns 30, he'll finally turn the corner and start to improve... I think the erratic K rate projections are mostly to blame here.

Regarding number 6, wouldn't an ideal projection system try to determine customized past season weights based on age/experience to handle the outliers (very old and young)? It appears PECOTA does not. That is, one would want to look at what the optimal weights are for a 22 year old vs. a 40 year old. There is almost no chance they will be the same as a average-aged player (recent perf. will in fact have more value, as will the rate of change), so why force them to be static in the projection system? Yes, I know that an aging curve is applied after raw rate stats are projected. But this assumes that all players age identically, and that there is zero information contained in the rate of change of past stats as to how this particular player is developing/aging. Fine for a mid-career guy, but not so good for players in the middle of a steep incline/decline. Basically, you want to take more seriously very recent changes in performance when players are of an age when very large, real changes are likely to occur. Speaking of which, was the order of past seasons considered? That is, if a player had TAvs of .250, .280, .310 in his last three seasons, his next season projection would look roughly the same as someone who had gone .320, .280, .250 -- assuming I am reading the explanation correctly. I would that for any aged player, a clear trend (and you probably need more than 3 years to establish a trend) would merit some level of inclusion in the predictive model.

I'm pretty sure most people know AFL stats are going to be a tiny number of PA. The only reason to mention PAs is if the number is less than expected for the relevant league. And if so, it's just as easy to write, "in 130 PA" as it is "[130]". I.e., not sure what this article is trying to establish that couldn't have been stated in a single sentence.

Here's a look at one season: http://www.fangraphs.com/blogs/index.php/the-good-shapers-follow-up/

Given they amount of money they have spent in the last two drafts, I'd say they shouldn't be all that happy..

Will your formula be used for save projections in next year's PECOTA? Do you have a similar study on predicting pitcher wins and losses?

Is there any reason why BP does not provide park factors? Always been a mystery to me..

Would enjoy seeing a study of whether hitters tend to improve vs. a particular pitcher the more they have seen him. This effect does exist on a team level within season, but I have not seen a player-level study. Would be difficult to do a full-career experiment due to survivorship bias, but a 1-2 year study of # of PA vs. TAv would be interesting.

61 comments later...is there anyone here who would put Arthur Rhodes into a close World Series game -- for any reason? If so, would love to hear why. About half the people here (incl. Goldman) defend LaRussa, but no one has made even a shred of case that he is a major league caliber pitcher, much less a guy you would look to in a close post-season game. This is not Monday morning QBing. In the game in question, Rhodes' performance was actually pretty neutral -- i.e., the odds of winning were roughly the same when entered as when he left. It's more a question of expected results going forward.

I think it's even more remarkable that: 1) Wyers admits he didn't even watch the game he wrote about so critically. (Would anyone read a scathing movie review from a critic who merely scanned a plot summary in the middle of the night?) 2) In a subsequent attempt to cover for his error (i.e., to claim his point would still have validity in the game sequence that actually occurred), he proceeded to make a horrendously misleading case about whether the next inning would lead off with a pitcher or a leadoff hitter. Why so horrendous? His rationale is that, "The average team scores .60 runs in an inning where their leadoff hitter leads off the inning; when the pitcher leads off the inning that drops to .49 runs." This apparently offsets the benefits gained in the current inning. The problem is that the next inning is not the last inning. It seems to me that the goal in the 4th inning is minimize runs scored for the rest of the game, not for the immediate next two innings. I.e., if you are going to claim that run-prevention in inning N+1 is negatively affected by a certain strategy in inning N, at least take to time to measure its cumulative impact on innings N+2, N+3...9.

Where did you show that past high-leverage performance is predictive of future high-leverage performance? Especially specific outcomes of high-leverage PAs. A certain pitcher induces more flyballs in high-leverage situations over a single season (tiny, tiny sample), and he should be expected to continue to do so? I don't think so.

I'm just stunned that Rhodes is on a World Series roster right now -- regardless of the actual outcome of this game. Tony: step away from the 42-year-old, 89-mph-fastball, 5.90 FIP reliever. However bad LaRussa projected Motte to be vs. Hamilton, I fail to see how that projection could be worse than that of Rhodes pitching. Oh, and calling his heater 89 mph is being very charitable when you look at http://www.fangraphs.com/pitchfxo.aspx?playerid=1097&position=P&pitch=FA. He was in massive decline this year and was throwing about 86 mph by the end of the season -- unheard of for even the softest of LOOGY relief pitchers.

I think you'll find the answer if you look at the average horizontal angle a pitch comes in to a LHB and to a RHB. LHBs see more pitches that are moving from inside to outside than RHBs do. The umpire's strike zone is going to naturally shift to where pitchers are trying to pitch, even if that results in a shifted strike zone. By no means is this acceptable, but it's what happens.

Would be interesting to see if this age effect exists at all among college draftees.

Your assumption is that whatever beneficial effect of growing up as older than most teammates on little league teams has totally evaporated by the time the player is 17. In fact, the month-of-year-born effect (surmised to result in superior coaching during formative years, higher self-confidence, etc.) could very well have a permanent imapct and still be at work after a player is drafted.

http://www.socialproblemindex.ualberta.ca/RelAgeMLB.pdf

That would be great. Ideally, for each given catcher/ump pair, you'd like to be able to estimate the net expected impact on called strikes -- looking at independent catcher impact, independent umpire impact, and also any possible catcher/umpire interaction effect.

Never mind. It's discussed below..

Curious what the source is that you use for "runs per called ball-->strike." I've seen a few numbers thrown out there in studies, from ~0.08 to 0.25 runs/changed call. Do you have a view on what is accurate?

Well said. This article is pure hindsight. The advantage play was to do exactly as the Rays did -- until MLB changes the rules.

It comes down to whether you think awards should be given based on what "should have" happened or what did actually happen. I "happen" to think actual results should determine awards, regardless of the role luck is assumed to have played in said results.

Number of Goldman articles since July 1: 21 Number of Goldman articles mentioning the Yankees since July 1: 16 Both fair and balanced.

Melky Cabrera is "a switch-hitter who is a far weaker hitter from the right side of the plate"? His career OPS from the right side is .700, vs. .740 from the left side. A spread of 40 OPS pts. for a switch hitter is not large at all.

A system is indicted because it produced two pieces of independent data rather than zero?

Maybe Maddon read this: http://www.fanduel.com/insider/2011/05/27/how-important-are-hot-streaks-for-hitters/

"Matt Wieters displayed exceptional defense while becoming more of an offensive presence than he had been in his first two seasons. " I.e., "he shed his inexplicable 'Top 50 Bust of All-Time' label by...well, continuing not to resemble a bust in any way, shape or form." Although, to be fair, most of his recent HRs were appeared to be aided by a mysteriously gusty tailwind that would seemingly crop up mid-swing.

Agree. No idea where the preference for Mickey Mouse font sizes started.

You understand that FIP is not park-neutral, right? Parks affect HR rates, K rates and BB rates; therefore, FIP should not be compared across teams. Specifically, Arizona gets unfairly penalized in such a raw comparison.

Looks like a nearly-25-year-old scraping by in single-A.

Since this is as good a place as any to ask: why not publish a top 100 prospects list every month, 12 months a year?

Yeah, it's just sheer chance that teams with a tiny pool of potential customers tend to be the cheap-asses and the organizations in cities that could easily support five teams are the big spenders. Clearly they are all getting the same amount money thanks to revenue sharing. So it's just a question of whether you want to win or not, right? Gimme a break. Note to baseball: institute a salary cap, or reduce the number of teams (by a lot). Not that hard. Every other sport seems to get it.

Target Field has played about as neutral as a park can be, so that's not a valid factor. And his defense at catcher is average.

Another thought is to check if there is a correlation between prospect ranking, and performance rel. to PECOTA projection. PECOTA is probably the best thing we have for performance-based projections of very inexperienced players, since it tries to use minor league performance. I.e., you'd be trying to isolate (for a certain set of players with a decent-sized minor league career) whether subjective rankings provide any predictive information not already contained within the player's stats and/or the fact that he made it to the majors.

I like the concept here, but the problem is that Marcel uses an extremely generic (regressed) projection for young players -- much more so that with 3+ year veterans. So the "projection" for most 23-year-olds, for example, is going to be nearly constant. Thus, the above data basically shows that young prospects are better than young non-prospects, not that prospects are beating any actual performance expectations. The only time Marcel truly represents an estimate of ability is after the player has played a few seasons in the majors, which is not coincidentally when the effects described above start to evaporate. This is probably shown most clearly in the K/9 data, where the prospects and non-prospects are going to receive roughly the same Marcel projection for their first couple years (regardless of minor league performance), and yet by the process of self-selection, prospects as a group will always have a much better K rate than non-prospects. I.e., not sure that this study is actually measuring breakout likelihood as much as it is measuring raw performance. Perhaps taking a look only at player seasons that came after at least ~1,000 PA/300IP over the previous three seasons would make it more of a breakout vs. expectations measurement, though obviously you wouldn't have a lot of data from young players.

Bowden's and Hendry's situations were not at all similar. Bowden basically performed inline with his decaying payroll.

It's certainly implied in this article, but I think it needs to be stated explicitly: Hendry has had a top 10 payroll to work with for each of the past seven years, has enjoyed a well-below-average strength of schedule, and yet has a .500 record over this time span. That's fairly tough to do, even after factoring in some of the erratic behavior from ownership described above. Unrelatedly, I wonder if Dave Littlefield has kept his job.

If the A's ever did find undervalued short pitchers, they haven't lately. They have had just one SP (Gaudin) shorter than six feet since 2003.

Do you have a link to this study?

"the degree of race correlation in player comparisons was almost perfect (except that black and Latin players were, very rarely, compared to each other)" Can you clarify what you mean?

"The question isn't whether teams will give short players a chance -- the question is how much of a chance." Which is exactly what the author originally said. He never said, "short players never get drafted or signed." How does your point differ at all from what was presented?

"going over the cap" What the hell are you talking about? This isn't the NFL.

I read the St. Louis capsule three times. It still makes no sense. He's "disgruntled" and "streaky," so his ship has sailed? Same guy with a .300 TAv in 2010 at age 23... Who would want that?

Well, he was awesome in 2010, so not exactly a huge stretch.

The article itself doesn't mention anything about power or home runs (just hitting slumps), but then the player capsules talk mostly about HR and the author comments also suggest this is a power outage article. So I think there is a bit of disconnect between what article says it's about and then what is actually is about. After reading everything, I assume it was intended to be "the four biggest power disappointments among lower-tier players."

4. Yes

If you care to read his post, he is talking about adjusting flyball and groundball rates by park, which makes no sense and is not at all what I am talking about.

So, just to clarify, if you were to make a fair over/under line on Padres pitchers for next season HRs allowed, you would take the current staff's flyball rate, and apply the NL average HR/FB rate to come up with the number? Cool. As a guy who has made a living betting on baseball for the past 10 years, I would be more than happy to take your action.

Questions that remain curiously unanswered: 1) Why were PECOTA pitcher win-loss record projections so screwed up in the pre-season? 2) What was changed in the PECOTA rest-of-season weighting scheme a few days ago, and why was it done? 3) Why is it assumed that a good test of ERA estimators is using the past 1000 IP to predict the next 200 IP? 4) Why was the park environment ignored in assessing HR rates vs. flyball rates as predictive indicators? 5) Why was schedule ignored in assessing HR rates vs. flyball rates as predictive indicators? 6) "Aren't you graphing actual HR rates vs. predicted HR rates based on flyball rates? Of course the actual HR rate is going to be wider--that's the nature of real life vs. projection, right? Are you implying that the distribution of expected home runs based on contact rate is wider than that for fly ball rate?" 7) "A good test might be to ask whether HR/FB is more predictive of second half home runs than HR/CON?" 8) "...there is a reason that this sort of analysis can be unsatisfactory: it assumes that a fly ball that wasn’t a home run is equally predictive of future home runs as a fly ball that is a home run." But isn't this just as true as HR/CON? In fact, couldn't you turn this argument around and say that this is why HR/FB is preferable to HR/CON? Because fly balls are more predictive of future home runs than, say, ground balls?"

Yep. What we need is consensus. Not innovation.

"To the first, all I can say is this - up until the point that this article was published, SIERA was our own metric." This is, of course, untrue. The article was published on July 25, a full week after SIERA was introduced at fangraphs.

It sounds to me like you have thrown your hands in the air and said, "FIP is the best that anyone could possibly do." How does that benefit subscribers? Unrelatedly, what metrics have you developed since your start date at BP?

I would challenge anyone to find a worse-formatted, more-difficult-to-use page than: http://www.baseballprospectus.com/sortable/index.php?cid=975549. Assume creation date = > 2002.

http://writing.colostate.edu/guides/researchsources/includingsources/quoting/overusing.cfm

Anyone?

The only person misusing SIERA as a long-term projection system is you. How about looking at your own article, and its odd, self-congratulatory conclusions about flyball rates? No one seems to be able to replicate your results on other data. Nor would any rational baseball person ignore park effects on home runs in such an analysis.

I sincerely hope this translates into more tangible gains for the subscriber in years to come. Thus far, to be frank, you have had a few very interesting articles, but the net data analysis gain since Wyers has joined has been pretty well hidden, if it exists at all.

Will there be a full article describing the development of the algorithm used to create the 10-year projections?

I agree with the aging curve being pretty far off -- both for plate appearances and for TAv. It's both way too optimistic and also too erratic (some players will jump from .230 to .260 and back to .230 in the long term projections).

Nice to see Colin run away from responding to comments on his own article while talking the time to spew snide tweets all weekend. E.g., "cwyers Colin Wyers I developed a new ERA estimator: 1*ERA+0. The r-squared is f@cking fantastic." Stay classy.

Oh, it will drop the home page and will be forgotten. I agree. But will Wyers bother to answer the many valid questions raised in the comments about his approach, or will he ignore them and wait till the article disappears? Based on past history, it will be the latter.

Yet another problem is the fact that pitcher schedule strengths are correlated within season and year-to-year. So if a pitcher faces an abnormally HR-heavy set of batters in a given year, chances are the following season will also have a higher than avg. percent of power hitters. xFIP will suffer because it explicitly assumes the pitcher will be facing a league-average schedule. The goal of estimators like xFIP try to show what HR rate a pitcher would likely yield IF he was magically placed in a random/neutral park against a random/neutral schedule. Wyers tests what happens when the player is NOT in this random/neutral environment, but rather in one that is highly correlated to his previous (known) environment. There is no reason one should try to use raw xFIP or SIERA to try to predict a season in a partially-known environment. If the goal of these metrics is to to predict next season accurately (which Wyers seems to be assuming), the correct method is to estimate the biases of each pitcher's next-season environment and make the necessary adjustments to xFIP/SIERA formulas or to the stats being measured.

I'll ask Lindbergh and Wyers again: what was changed in the PECOTA rest-of-season weighting scheme a few days ago, and why was it done?

As some feedback for next season, I don't think the current presentation of UPSIDE is as useful as it has been in years past. I don't think most people care what the exact upside prediction for 2017 is (down to a decimal point); they would like an approximation of the player's total upside over the next 5 years, or 10 years. Upside used to be listed this way (as a total). Now it's either listed for 2011 only (in PFM and the PECOTA spreadsheet), or as a row of individual year numbers on the PECOTA cards. Also, it was mentioned several times in the pre-season, but the order of tables in the PECOTA cards seems odd. The injury and contract info. tables, which are not of primary interest to most users and take up a ton of room, are listed above projected playing time, forecast percentiles, and now the 10-year forecast.

Something tells me that if xFIP and SIERA were adjusted properly to reflect the pitcher's expected park environment (which is already built-in to ERA and FIP to varying degrees), the avantage would shift a little more to xFIP and SIERA. xFIP and SIERA were not designed as projection systems; if they were, they would certainly contain a simple park adjustment to reflect the actual environment (avg. HR/FB) of the player in the year being projected. So if someone decides on his own to use them as projection systems (as Colin does), he needs to make the adjustments to make it a fair test. Obviously, not every pitcher remains on the same team year-to-year; but most of them do. Same thing with fielding: there is going to be a reasonably high correlation of team defense year-to-year, which will help ERA as a predictor and hurt the fielding-independent estimators. The short version is that if something is designed to neutralize park and defense (as FIP, xFIP and SIERA are to various degrees), it's not right to use it as a raw predictor of something (actual ERA) that is affected heavily by park and defense.. Either make a new metric that is designed to be a predictor of raw stats, or create a new test.

More critically, it appears you fail to control for park in your comparison of flyball rate vs. HR/contact rate in predicting future home run rate. Clearly, you would not use a league-average HR/FB rate to predict the second half of a pitcher in an extreme park. No one thinks that HR/FB rates regress to MLB-average rates. They regress to park-average rates (the net HR/FB park factor of a pitcher's schedule). So no one would use a generic HR/FB rate or raw xFIP to predict second-half ERA. You'd obviously under-project HR rate and ERA of every Rockie pitcher and over-project every Padre by doing so. A stat like xFIP is only useful when compared to itself (across pitchers on same team, or across years of same pitcher); it's not well-designed to directly predict actual ERA and I don't think this is a particularly new revelation.

Forgive me if I glossed over the three-quarters of the article devoted to dated politics, but it seems your sole point is that managers should pull closers as soon as they blow a few saves. It's hard to think of a policy that would fly more in the face of modern statistical analysis. What you are suggesting is that managers make personnel decisions based largely upon whether four or five save opportunities were converted or not. That's not just recency bias; it's also using arguably the game's most flawed statistic (save pct.) to gauge that recent performance. It's a binary stat that has incredible variance independent of player skill. If a manager were to follow your advice, he'd be endlessly chasing the "hot hand" rather than playing the players who gave him the best chance of winning. If a guy's peripheral stats are awful for a couple months, obviously he should lose playing time. But you are not saying that. You're saying managers should kick guys out of high-leverage roles as soon as they blow a few saves: "most of them should be about as secure as the average NFL placekicker—you shank a couple, you’re out of a job." Bizarre to see that on this site.

Can you summarize what changes have been made? Not trying to be snide; I just don't have a handle on what has been added/improved from the data reporting/analysis side.

Do any of the year-to-year tests here adjust for park? If not, some of the error observed is not actual error, but a park bias. I.e., for a given park (in cases where pitcher stays with same team for both years), a pitcher should be expected to have an actual ERA that is higher or lower than his estimator ERA. (E.g., a Padres pitcher will always be expected to have an ERA lower than his xFIP [Petco depresses HR/FB], and xFIP should not be penalized for this since it was not designed to be park-dependent). This will vary by estimator, as some assume league-avg. HR/FB rate, etc.

Who is using ERA estimators on a full season to predict the next season? That is what projections algorithms (which use more than one year) are for. ERA estimators are useful for very small samples (50-100 IP), and should only be judged on that basis.

Why would simplicity matter very much for an estimator? Are people jotting down FIP calcs. on a napkin, or looking up the final numbers on a web site?

At least Swartz is 100% transparent in what he is doing, and is very responsive to reader input. Contrast that with BP, which has apparently changed (improved?) PECOTA, but has published nothing to indicate what has changed or why. We were all expecting a massive research piece on what steps were taken to improve the algorithm this past off-season. Instead we got crickets and a late product. Now we get an incredibly time-consuming rebuttal to someone's else's work on a different site. This off the heels of an even more off-target critique of Fangraphs' rest of season weighting algorithm. Exactly what weighting are you using again for PECOTA RoS, Colin? Oh wait: you never answered that repeated question, instead spending a whole article doing forensic analysis to uncover and slam someone else's algorithm. I have no allegiance to Fangraphs or BP or any other site for that matter. But the trend here at BP is undeniably disturbing. More effort is being put toward saying someone else is wrong than it is toward creating and optimizing your own models and tools -- at least based on what is being published, which is all that really matters. The PECOTA pitcher win-loss projections were unbelievably screwed up, beyond any shred of credibility; the 10-year projections never came. But instead of reading articles about what happened and how it will improved for next year, we're reading about how some ex-employee should stop trying to improve a once-prized algorithm for estimating true pitcher skill over small samples? Color me saddened as well.

Settle down, Beavis. You made an absurd comment/accusation, and got called out for it.

I would agree. But a single season clearly is not going to prove a whole lot.

One more unsolved pre-season mystery: why did the projected team win-loss records differ between the Postseason Odds and Depth Charts reports? Both should have been using the same data (projections and schedule), yet there were differences in the numbers.

Not sure what you mean. What park environments are rest-of-season forecasts assuming?

"After the season, I will take a look at how each measure performed, and that may go further toward resolving the disagreement." Wouldn't you want to backtest this over, say, 10 years of data instead of just 2011?

More info.: http://www.fangraphs.com/blogs/index.php/new-siera-part-two-of-five-unlocking-underrated-pitching-skills

If we are bringing up unresolved PECOTA/projections issues, here a few [all of these issues were raised in the comments, but not answered]: 1) What mix of projection vs. current season does the in-season Postseason Odds report use, and how was this weighting algorithm tested? Why is this system not transparent, and why not show both the projection team strength and current-season-only team strength, so people can see the actual team vs. expectations. 2) This page, http://www.baseballprospectus.com/odds/ is, to use one of your words, wrong. Look at the expected win pcts. Oakland = .641; Colorado = .627. 3) Why were pitcher win projections massively inflated in pre-season PECOTAs? E.g., all of the Mariners pitchers were projected with a W-L record >= .500, yet the team was projected with 71 wins. Many other teams looked as bad -- e.g., Twins and Mets. Overall, there was a huge problem with PECOTA pitcher W-L projections, an issue which was first raised in early March. To my knowledge, it has never been addressed.

Please change "Expected Win Pct" to a name that is more accurate. It is not the expected winning percentage of the team; it's the true, schedule-neutral strength of the team.

Is it safe to say RoS PECOTA is using park factors only from 2010-2011? If so, that does not seem wise.

Either way, we need to know what exactly the new weighting scheme is and why it was chosen.

I appreciate the update. But I am still left to wonder: what is the current status (and future) of the missing 10-year player projections?

Well, the question is how much cash the Jays paid the Dodgers to take Rivera.

Well, if a team knows that going with a KRod/Axford sequence in the 8th/9th is going to save $14 million over using an Axford/KRod sequence, I think it's pretty obvious what that team would choose to do.

Here's an idea: if you're a player, don't agree to huge incentives based on counting stats.

I'm sure if the Cards want to pay $10 million for half a season of a good reliever, they can find other options...

Look up the actual research on the topic. It's been done. http://www.hardballtimes.com/main/fantasy/article/do-hitters-decline-after-the-home-run-derby/. There is no need to use anecdotes to speculate.

No one harps on an obvious point more than Sutcliffe. I'm including the ghost of Joe Morgan. I have yet to finish watching one of his games.

"With the power play, the home team outscored the visitors 30-25, or by 20 percent" Some something happened during one season 30 times instead of 27.5 times, and that's conclusive? If you genuinely don't understand why this is a negligible sample, I can't really help you.

The goal of a salary cap is not to force parity; it's to make skill the primary factor for success (vs. squatting on a premiere market and/or entering the business with cash to burn).

So Chamberlain would be a much better pitcher now and/or would never have become injured had he not been "mishandled?" Please explain.

Sweet nit to pick. So the scorers are actually the ones involved in the conspiracy... Even more likely.

Where do get the idea that Wieters has already played a 10-year career of mediocrity?

You don't seem to comprehend position scarcity, among other things.

It's not Wieters fanboys who were offended by your absurd and continuing claim that he is a bust. Rather, it's every rational baseball fan who happens to understand that: 1) In order to be a "bust," by definition, most players drafted after you need to have outperformed you. To date, Wieters has a career WARP of 3.9. Only three players drafted in the first 20 picks of 2007 have achieved a higher WARP than Wieters since the draft. If you want to look at just the college guys in the top 20, Wieters ranks 2nd out of 8, behind only David Price (#1 overall pick). I.e., exactly where you would expect him to be, if not a little higher. 2) Calling anyone a "bust" at age 24 is risky, unless he has already experienced massive failure. But calling someone an all-time bust, when that someone is still in very beginning of what looks like will be a long career...well, that's just desperate page view grubbing. You're basically projecting your own misguided expectations (that apparently a top 10 draft pick should be an All-Star immediately) onto the rest of world. The original article you wrote was an embarrassment to the site, but not nearly as much as this follow up. I mean, if umpires are conspiring to keep Wieters' passed ball numbers down, who knows what else they are doing to help him along and try to make you look bad? I'm sure you'll think of something by the time you do the next follow up.

Wrong answer. It's not the pitcher's special abiility to control HR allowed that causes this descrepancy; it's the park. This is why there is a large diff. between xFIPs of Giants pitchers and ERAs of Giants pitchers historically. xFIP assumes a normal HR/FB rate but the SF park has a very low HR/FB rate. So much of Cain's "beating" his xFIP is very much expected and contained within the limitiations of the xFIP formula.

I'd rather see the hot vs. the hot. Just like I prefer the World Series champion to be whichever team was hottest during a given season and a given playoffs -- not whichever team has the highest lifetime achievement.

You understand that most Giants pitchers have a better xFIP than ERA historically, right? Look up the formula for xFIP and tell me why this occurs.

I don't think it's an accident that the year Lee became great, his velocity jumped up by 1.5 mph. Similarly, Santana was one of the harder thowers (93 mph) in his great years. I.e., studs rarely emerge without better than average velocity -- regardless of what their main out pitch is.

I like the premise of this article, and hope to see more lists like it (mid-season views on who has the highest upside in the minors).

I agree. This an angry, angry young man -- I mean, article. For very little impact. I personally would prefer to see the 10-year PECOTA projections arrive sooner than a random rant against a fairly innocuous opponent. Four months and counting.

So, you are performing the following calc., right: (ExpYear2-ExpYear1)-(ControlYear2-ControlYear1)? And the result is construed as the change in skill for year 2? I think I get it now... My only remaining concern would be if the avg. age of the exp. group was for some reason trending one way over a decade or so, it could lead to erroneous conclusions about true player ability changes (which would be better explained by changes in how aggressively/cautiously players are brought up from the minors). No idea if this is the case, but just mentioning as a potential factor.

And any other team would have taken one look at him and said, "Yep, this looks like a textbook 9-month concussion. Don't even let this guy think about coming back until next year"? Don't think so. Unfortunately, that's not the way concussions work.

The problem I have is that you are measuring the diff. between how given players perform vs. past opponents and comparing it to how they perform vs. brand new opponents, during a given season. From that comparison, you are concluding that strikeout skill is going up for pitchers each and every year. For example, looking at the year 2 comparison of control vs. experimental for pitchers... I would argue that the experimental bin for these pitchers will contain an abnormal pct. of rookie batters or other young batters whose playing time has increased from year 1 to year 2. A batter with increasing playing time from year 1 to year 2 will be more likely to have his plate appearances qualify for any given pitcher's experimental bin. Younger batters tend to strike out more. The net result is that the experimental bin for a pitcher in year 2 will contain more plate appearances vs. high-K batters than the control bin will for that same pitcher. So the effect of pitchers' improving K skill every year may just be a measurement of the bias between the control and exp. groups. A similar and additive bias should also be present in the year 1 control vs. exp. comparision. The exp. group will have an abnormal number of players with declining playing time (older players), which will depress observed K skills for pitchers (older batters are harder to strike out). Since you are comparing year 2 to year 1 to assess pitcher skill change, this will further inflate the increase in K skill. Overall, comparing the experimental year 1 results with the experimental year 2 results requires the assumption that the two experimental groups of opponents (batters) are roughly equal in skills. I'm not sure they are. Does this make sense?

"Dickered endlessly?" To my knowledge, Morneau has had more time off due to a concussion than any other baseball player. Long-term brain damage due to a concussion is nearly impossible to assess. You cannot see the damage done and therefore cannot see if/when it has healed. So the Twins' approach, in my opinion, is what any other team would have done. But you are saying that most teams would have helped Morneau's brain heal better?

Thanks. But wouldn't the experimental group (matchups between guys who did not see each other last year) be over-represented by young players? It seems like young and/or injured-part-of-last-season players would be selected for abnormally in the exp. bin, as they would often have far fewer matchups last season vs. this season. I.e., they would generate a disproportionate pct. of the "new" matchups this season by virtue of their limited playing time last season. I'm not sure I am interpreting your methodology correctly, so the above comments could be off. But maybe you could write a follow-on article to break down your exact process and also what each graph represents in more detail.

This article is much more pro-DePodesta than it is anti-Dodgers. Remind me again what DePodesta has accomplished after being liberated from the Dodgers?

It would be nice to know how exactly PECOTA projects wins, as it had every single Seattle pitcher with a >= .500 record this pre-season, and yet the team was projected to win 71 games. I still have not heard an explanation for this. "Or I’m sure PECOTA does something similar to arrive at a pitcher’s projected win total, so when looking at a pitcher’s projection, don’t just look at the ERA and call it a day."

To follow up further: in order for SIERA to be systematically inflating numbers, something fundamental would have had to have changed in how runs are scored this season. That is, even with a lower-offense environment, the peripheral stats SIERA uses should be reduced proportionately, thus leaving the algorithm to work as is, without adjustment. Unless I am totally misunderstanding how SIERA is calculated.

Not sure I follow the ~7% discount applied to Stauffer's ERA. Is it correct to assume that SIERA is overestimating by 7% this season?

http://claydavenport.com/stats/pagePITyearALL.shtml

So, essentially, your "experimental" group is comprised of baseball's youngest players, correct? And these youngest pitchers have been improving their strikeout rates at an average of ~1.5% per year -- so ~15% aggregate over the past 10 years? Just making sure I am interpreting correctly. Meanwhile, the veterans have been striking out fewer and walking more batters. So the conclusion is that rookie pitchers over the past couple of years have driven the overall decline in run scoring?

Beg to differ regarding Gorkys Hernandez. The guy is almost 24, and hitting a DT-translated .226 EQA this season. That doesn't bode well for his future, regardless of his CF defense. Davenport projects him with a peak OPS of .630.; Oliver projects a peak OPS of .597. Both of these factor in his current-season performance.

Is there evidence for this? That is, do infielders have strong year-to-year correlations for pct. of BIP converted to flyouts credited to the infielder?

How does a 3.50 SIERA for Stauffer equate to a projected 55% team win pct. in games he starts? SD starting pitchers have had a 3.68 ERA YTD (only slightly worse than Stauffer), and the Padres' 3rd-order win pct. stands at .406. A 0.18 improvement in SP ERA is worth roughly 3% in expected win pct., so the end result should be in the 44%-45% range -- nowhere near 55%.

That's if you assume that whatever phenomenon causing past data to lose its predictive value over time is generated solely by a ticking clock. The alternative hypothesis is that a player is more likely to implement/undergo fundamental changes in the off-season (new delivery/stance, fitness change, nagging injury healing, etc.), and so the existence of the off-season would cause more information value to evaporate from the past performance than would the passage of the same amount of time within a continuous season. If the theory is true, April's performance would be more predicitve of same-season October's performance than October's performance would be predictive of the following April's. Hence, the question.

Also, can you post a link to your study?

If the optimal weighting scheme is that well known and accepted, I'm not totally sure why none of the three in-season projection systems (ZIPs, Oliver and PECOTA) are using it. Or is PECOTA now using that weighting scheme? Does "daysAgo" refer to calendar days, or days of a season?

I find it hard to take a prospect seriously when he (Iglesias) has yet to hit a single HR in 450 career pro plate appearances. The guy has just *one* extra base hit in 150+ ABs this season. Just 3 out of 5 in SB att., so it's not like he's a legendary speed burner, either.

I don't think anyone is questioning how the theory of weighting by PA/IP works, or how one would implement it. The question is what are the optimal weights to use to provide the most accurate in-season forecasts? It's an interesting problem and one I would like to hear from Colin on.

I also am more interested in what PECOTA is doing *right* than what Fangraphs is doing wrong. I.e., what weighting per stat is in-season PECOTA using and more importantly, what was the method used to arrive at these weightings?

I hear you. Obviously, a high-quality rest-of-season projection is what we are all looking for. But until we are there, people continue to generate lists if xFIP to ERA, or SIERA to ERA differences mid-season for fantasy purposes. My argument is that these are not good tools to use to evaluate what a pitcher "should" have done thus far this season. We agree that xFIP and SIERA are not great raw predictors of ROS stats, but this presently is not stopping people from using them exactly as such over and over. I'm all in favor of quality ROS projections, but I believe there is still a need for a single-single season ERA predictor metric -- a SIERA-type stat that aussumes the player's environment is not changing. THis would satisfy those of us who want to get a simple look at how a pitcher is truly performing in the context of his current team/stadium, without the baggage of an opaquely-weighted multi-season projection.

You should take a snapshot of ROS projections from Fangraphs and from PECOTA right now, and then evaluate at the end of the season. A single year won't prove anything conclusively, but if the difference in accuracy is huge, it will certainly suggest which method is superior.

I don't follow why MRIs are not done immediately for any pitcher who is showing any kind of arm issue, and then even routinely (every couple months) for pitchers without known issues. It's not like the $500 per MRI is going to put a huge dent in a team's finances.

It wasn't just Baltimore who roundly mocked your selection of Wieters; it was a wide range of rational baseball analysts who were stunned someone would mistake a solid career start with a historic bust. In reality, his 2011 is on pace to be worth 4.5 wins in his age 25 season. Not exactly busty...

I would agree. An "About Us" page with all current writers, their titles, bios and dates of hire would seem quite useful. My impression is that BP is currently operating a merry-go-round. Every week, there is a brand new name or three on the front page. I'm all for discovering new talent, but at this point the site feels much more like an anthology of independent blog posts than a coherent brand. It's interesting that page views are a criteria for rentention. I am all for this, but would hope that the concept would be taken a step further and a reader rating systems would be implemented. Something where readers could rate each article 1-5 stars. I feel like BP is never shy to judge others, and so should be very open to accepting transparent feedback from its customers.

The main problem with using SIERA (or xFIP) for fantasy purposes is that ballparks have a large influence on HR/FB, and these ERA estimators totally ignore it. E.g., as far as I know, SIERA/xFIP use the same expected FB/HR for pitchers for Padres and Rockies pitchers. Ditto, for defense. Assuming a MLB-average HR/FB rate and MLB-average defense is appropriate for evaluating true skill of pitchers in between seasons. It's not, however, a good practice for predicting second-half fantasy stats, which I believe more people care about. In other words, please fill this void and create a metric that assumes the pitcher will remain in his current park/defense environment for the rest of the season.

Was research done as to the optimal weight to give to in-season stats vs. prior-season stats [that is, what weighting system has best predicted ROS stats historically]? Or was it assumed that current year stats are exactly as informative those from three years ago (for veterans)? I.e., is there evidence that Fangraphs' weighting system is not optimal, and yours is? Also, I think there is a typo in Headley's numbers above. He has outperformed projections thus far, but his ROS is lower than his pre-season projection.

I think you're right that this is a bad example to use (the catcher in this case was not interfering with the runner), but that doesn't take away from the general idea Davenport is expressing, which is that catchers should not be able to plant themselves in front of the runner.

Well said. The current (lack of) rule is absurd, even without considering the injury increase factor. I've always wondered why putting your leg down right in front of the plate is any different than just sitting indian-style on top of the plate? To take it to a more ridiculous level, why not call in the firstbaseman and have both he and the catcher sit down on top of home plate, each vaguely trying to field the ball thrown from the outfield? I'm glad someone finally called out the shortcomings of the current rule, and hope MLB is reading this.

What happened to the 10-year PECOTA projections this year? Happening or not?

We're quite a ways off from the trade deadline, and odds are the Pirates will not be close enough to .500 at that time to make it a difficult decision. The decision will most likely be whether to try to get ~75 wins or 70 wins.

It's pretty unusual to hear that a guy (Cole) has increasing control problems when he cuts his walk rate in half (from 3.8 BB/9 in 2010 to 1.8 BB/9 in 2011). I understand that you are talking about recent trend, but I also understand that stats from a few starts tend to not to be very predictive... Ditto for his BAA. A bump from .205 to .231 is not exactly telling for a 92 IP sample.

What does PECOTA say about his 10-year forecast? That would seem to be the most direct way to assess his future.

Would love to see the old True Average report available again. It was the best way to see at a glance how each team's hitters were performing. The new format has the same information but much more difficult to browse at a glance.

Again, we need to know how actual stats vs. projected stats are being used to generate the expected win pct. As of now (May 4), none of us has any idea how much projections are being used.

I agree that a Transaction Analysis column, if it exists, should be broad and comprehensive. I disagree that the column was ever, or will ever be, great. It's pretty bland stuff for the most part, and when you de-flower Kahrl's prose, you're left with formulaic complaining about mostly insignificant events. I consider Lindbergh to be a significant upgrade over Kahrl in terms of overall insightfulness and ability, but...I'm not sure this column is the best use of his talents. Would love to see more in-depth work from Lindbergh on a wide variety of topics, and TA left to someone else, or even abandoned if necessary.

Yeah, this is getting (has gotten) bizarre. Make a big splash with a new stat that is supposed to be the best pitcher evaluation metric, and then not have it working for the current season (already in May)? Would be nice for BP to take a step back from the editorial fire hydrant and address the ongoing data issues -- from still-missing 10-year projections to missing current-season stats. Something like, "This is a black swan event. A, B and C happened. X, Y and Z are what we have done to prevent similar issues going forward." Alternatively..."This is just the way it's going to be. We'll update stats/projections whenever we can, but don't count on any predictable schedule -- now or in the future." I think people need to have an idea which one of these two is the current policy at BP. If this was a free blog, I'd stay quiet and enjoy my erratic updates. But a pay service? At least explain what customers should be expecting. That's all we ask for.

You might want to look more closely... In 2007, (the "89" mph season), he pitched 21 innings, so the sample is too small. Also, not surprisingly, his ERA that season was terrible at 5.48. So his avg. FB velocity in full seasons was: 2006: 91.5 2008: 91.2 2009: 91.5 Hardly a lot of variance. Yes, within that context, 1 mph is "significant," as I stated. Never said the difference was "massive." Again, read up on the subject at http://www.hardballtimes.com/main/article/lose-a-tick-gain-a-tick/

By all means, do a study with a sample size of two....or look at the work of people who have analyzed the entire league.

Shockingly, Wright hit 12 of his 29 HRs at home in 2010. Just slightly less than half his HRs erased by Citi Field..

This is pretty obvious... If it is agreed a guy would hit 30 HR in a neutral environment, and someone claims due to his actual environment he will hit 15 HR, it means his home park (where half the games are played) is being assumed to allowed zero HR. Not that tough to understand.

Will the 10-year PECOTA card projections ever be released this season?

Glad you noted this. I feel like it's one of the better kept secrets that most pitchers lose velocity starting pretty early in their careers. Sort of off-topic, but do you know of a study on the aging curve of GB/FB rates for both hitters and pitchers?

Can we at least have an indication of what the current PECOTA weighting is?

As a control, you would need to look at how hitters perform after missing ~8 games due to something other than a concussion. I have a feeling that taking time off is a generally detrimental to hitting, and might very well explain the 6 points of TAv difference.

I'd prefer to see more analysis in this type of column. I think if one is going to offer a column with betting picks at BP, it should try to find a quantitative edge instead of saying a certain pitcher is "due" for a win, or that a certain team is abnormally poor on the road, based on last 50 games (negligible sample). Personally, I have profited from betting against this kind of mentality. Most trend-type observations are pure noise in baseball -- whether it's a pitcher doing really well for 2 years in day games, or a team going winless in its last 10 games vs. a certain opponent.

Not wasted, but certainly a poor investment, even for a team that is trying to overpay for talent.

Any clarification of this?

This report needs the team's actual W-L pct., as well as the current weighting of PECOTA vs. actual that being used for the expected win pct. Overall, a smaller, more professional/readable font, and more data (like last year's report) would be helpful. Thanks.

Sorry to rain on the lovefest, but I have always found Kahrl's writing to be labored, convoluted and oddly condescending. Her columns read as if they had been written by Warren Buffett chastising beginning traders for the smallest of supposed trading errors. The most obvious problem is that Kahrl has never had the credibility to cop such an attitude. What teams has she managed? What groundbreaking research has she contributed? It's fine to answer "none" to both questions, as long as one keeps the snideness in check. Instead, it is always an over-the-top, "what have these idiots kids [GMs] done now?" mentality that is tough to tolerate, much less enjoy. The more fundamental issue is that the subject matter (largely insignificant transactions) does not warrant such a high level of attention/resources, whether in the form of run-on sentences or not. I happen to like Lindbergh's work thus far, but feel that his talents would be better used in generating new analysis. Let's allow TA to die a peaceful death, and focus resources on innovative work.

For the same reason that Nate Silver decided to put his work on BP 10 years ago, rather than simply joining every hi-stakes fantasy league he could find.

A) If you know what you are doing, you know that Accuscore is not very good. B) The point is to go through the exercise of what goes into predicting the outcome of a game. That is what people would find interesting.

Interesting column. Would love to see some of the BP staff try to develop a simple model to predict the odds of team winning a given game. I think it would be an entertaining project to document as enhancements and improvements were made to the model.

Would love to have the option to turn on/off PECOTA adjustment. It would give the user more flexibility to decide if he or she wants to use only current-season stats or not. Thanks.

Please consider adding back to the Adjusted Standings report each team's AEQR scored and AEQR allowed (3rd order). It's the only way to see the schedule-and-luck-adjusted offsensive and defensive strength of teams. This info. has always existed in this report. Please put it back in, or make a different, more detailed report avail. with it.

Please put back in this report 3rd order runs scored and runs allowed. It is very useful for measuring a team's adjusted run creating vs. run preventing ability at a glance. I would recommend including this data in the current report. Or, you could make a separate, more detailed text report for subscribers seeking this data that is no longer shown.

Are the PECOTA 10-year forecasts coming out this year, or have they been cancelled?

Where are the 10-year projections?

When are 10 year player cards due?

Add a col. for the 10-year upside, and put it on the card as well.

Why on earth would you restrict a minor league player's comps. (and thus his Upside calc.) to major league players? That defeats the point of using comps. for prospects. I fear the 10-year projections for prospects (probably the single most valuable output of this whole process) will be ruined by this policy.

That's just a poor calculation of variance in a projection. No player should have an 80% chance to finish within 3 HR of his projection...

Pretty interesting. Looking forward to more of this during the season. One observation is that run scoring is actually positively correlated with temp., so the reduced air resistance appears to benefit hitters even more than the added velocity benefits pitchers. How was the population chosen for the temp. analysis? You only used guys who played the whole season I assume?

Projected Playing Time (with projected stats) should be moved to the very top of the cards. Right now, if you want to know how many ABs BP is projecting for a given player, you have to scroll down 2 pages.

As stated by another user, Upside should be listed as a 5-year or 10-year total value -- both in the Pecota cards in in the PFM spreadsheet.

Did I miss an update on the 10-year projections? Maybe for the All-Star break? Seriously, I don't understand how BP management (assuming there is such a thing) can remain silent for a month and a half while a core product is MIA. I've never quite seen a company (fail to) work like this..

Speaking of Lindor, what would you say the odds are he stays at SS long-term?

Can you also address the inflation of wins projections for pitchers? E.g., why every single Mariners pitcher is projected to have a .500 record or better when the team is projected to be 70-92. Obviously, at this point, there is no way one can trust your pitcher win-loss projections. But I would like to hear why this issue has not been fixed or even so much as acknowledged by BP in the five weeks since it has been pointed out.

Better late than never. But I think we could all use a coherent explanation of what happened over the last four weeks. I am pretty shocked, given the magnitude of the issues last season, that the apparent bugs preventing a timely production of long-term projections were not addressed before spring training.

The spreadsheet should show upside ratings...

Yes, injury, compensation and fantasy sections should be at the bottom. 2011 forecasts should be up much higher.

It would make sense to test any defensive metric to see how predictive it has been historically. I suspect the predictive value of FRAA would be quite low, but I would certainly be open to reading an analysis of it.

Does anyone at BP care to address the pitcher win issue? E.g., take a look at the collective W-L record of the Mariners' pitchers, and compare it to the team's projected W-L record. Not even remotely close (all the pitchers are projected to be above .500 whereas the team is projected to be 70-92). Same with many other teams. I.e., there is a major problem with the pitcher W-L projection algorithm. This problem was pointed out more than three weeks ago. I have yet to see a response from BP.

My top concern is his velocity drop last season: 91.1 mph avg. fastball, which is a career low.

Your scoring system should penalize Phils' starting pitchers, if anything (no wins). But I have to say this seems like a pretty random, borderline useless BP article (trying to show that one draft has a home team bias)...

I don't really see why anyone would have suspected that quality start pct. did not correlate well with avg. runs scored, or with ERA, or with team win pct. I.e., I don't see why this analysis was performed or what it really proves. The question is, what additional value (beyond RA or ERA) does the quality start metric provide? I would say not much. It shows how well-spaced runs allowed have been for the pitcher. The problem is that this is not likely to be a skill -- i.e., past run spacing is not predictive of future run spacing. And if you're in the market for a stat that is not predictive of future skill, you might as well use W-L record.

Did the Marlins steal tens of millions of dollars from Willis when they paid him only $200k-$400k in his first three seasons? Draft picks are gambles; contracts are gambles; life is a gamble. The Tigers gambled; they lost. Get over it.

I would pose this on a Wyers article, but since he has not published anything since Feb 24, I'll ask it here: Where are the player card updates?

Just curious: where do you get this data for spring training games? Is it publicly available?

Unfortunately, there is no "90% PECOTA" this year. BP (apparently) has stopped updating player cards for 2011. So all the percentiles and upside numbers are still from pre-season 2010. Even the bio info. is old. Nice work...

I certainly wouldn't trust a 1-year trend to be predictive. If you look at his numbers from 2008-2010 (http://sports.espn.go.com/mlb/players/splits?playerId=5641&type=pitching3&three=1), he was actually slightly better on no rest (lower OPS allowed) than on one or two days rest.

These are valid points. I just get the feeling that the fantasy stats forecast algorithms are half-baked at best. No one seems to be in charge of the full process. And certainly the process of projecting stats, esp. counting stats, is a complex one. The main issue I have is the dead silence from BP whenever a discrepancy/issue is pointed out. E.g., the bizarre win-loss totals for pitchers. The tiniest bit of responsiveness and/or transparency would go a long way to improve BP's eroding credibility as a top source for fantasy projections.

It's hard to be bullish on a guy whose avg. fastball is 88 mph and at best, and *might* have regained some semblance of control. Would be interested to hear how hard he is throwing with his new delivery. Also, FWIW, his downward spiral did not begin after he was traded. His xFIP in his last two seasons with the Marlins was 4.48 and 4.74.

I find it odd that every pitcher is green for odds of missing 30+ days, whereas less than half of the hitters are. This doesn't jibe well with subjective experience, which is that pitchers have more major injuries than hitters. Is there something unusual about this team, or are pitcher injuries less common that people typically think?

Random question for Corey: Do you think that teams should schedule routine (every 6 mos. or so) MRIs for pitchers? It seems like MRIs can often show partial damage to a shoulder or elbow. They're non-invasive and pretty inexpensive relative to payroll. I've always wondered why MRIs aren't used more in baseball for proactive screening, instead of just to investigate symptoms.

A salary cap is not even remotely similar to tenure. Tenure is an agreement educators make with their employers to protect their jobs/benefits indefinitely without regard to job skill/performance. A salary cap is an agreement between organizations to limit spending in order to make a sport measure skill instead of wealth. Think of it in terms of a fantasy league. Tenure would be guaranteeing each owner the right to continue to participate in the league no matter how much his/her effort or skill level dropped. A salary cap would be limiting how much each fantasy owner spent in the draft each season. They are totally different concepts. Plenty of franchises in salary-capped leagues have gone under, as they should. Putting a cap in place does not mean granting each city with a current team the right to have a team indefinitely.

Well, it's pretty obvious that the players would be against a cap on their total income pool. But that's not really the point (employees don't usually get everything they want). The question is what is in the best interests of the fans and the best interests of the long-term health of the sport. Right now, the players are clearly winning. The majority of fans and the long-term interest level in the sport are losing. This can, should and will change eventually. As for "parity," the goal is not to create parity. *If* it's an equal playing field, and a single team dominates for a long period of time, that is an amazing and highly respected accomplishment. If, on the other hand, a baseball team dips into its owner's pockets as much as it wants and uses its geographical fourtune to its advantage year after year...well, then you have no idea how much skill vs. situational advantage was involved, and the accomplishment is diminished. Baseball is the only sport in America where a GM who has not even sniffed a championship series in his 13-year career is considered a genius. I'm not even saying Beane is or is not a great a GM. I'm saying that the current structure of baseball makes it difficult to to know how good he or any other GM is; and any time a sport is failing to measure/reward skill accurately, it's a bad thing...

No one is arguing that a salary cap would make any specific team profitable. People in favor of a cap generally simply that the sport become much more interesting once you start separating baseball skill from the geographical monopoly/duopoly a team happens to have been given.

Either reduce baseball to ~8 remaining teams, or put in salary cap. No other solution creates a reasonable sport.

Way to find the outliers... The relatively high variance in baseball does not make it a fair sport. If I wanted to root for giant corporations to put up better numbers year after year over smaller firms, I would join a stock market fantasy league and wear an IBM jersey. Sports is about creating an even playing field and measuring athletic and strategic skill. The only way to do this -- and most other sports understand this fact -- is to strip away money from the equation as much as possible. I can't imagine anything less satisfying than being a "fervent" Red Sox or Yankees fan. If you lose, you did so while having 4x the salary of many other teams; if you win, you did what a team with a massively unfair advantage should have done. To me, that's not what sports is about. It's about creating a structure that measures true skill.

Regarding Francisco: Last I checked (http://www.baseballprospectus.com/unfiltered/?p=669, among other studies), looking at 180 AB in a single season to assess apparent L/R split trends is not useful. He has a career .806/.762 L/R OPS split (i.e., normal), and has seen his fair share of RHPs over his career (782 AB vs. RHP, 311 AB vs. LHP).

Ideally, the projections would have a column for "expected PA/IP missed due to injury." That way, people could figure out how much of the playing time forecast is based on injury expectations, vs. other causes of less than full playing time (platooning, position competition and other benching).

The projected TAv's in the PFM do not match the player card projections for many (most?) players. No idea what is going on.

There has been a significant and inexplicable lack of response to major issues raised by BP users over the past three weeks. E.g., Where are the player card updates (incl. 10-year forecasts, percentile forecasts and upside projections)? Why are the projected records of teams different between Depth Charts and Post-season Odds pages? (Are they using different schedule assumptions; if so, how/why?). No, Colin's article (http://www.baseballprospectus.com/article.php?articleid=13040) does not answer these questions. Why are some of the projected team records from PS Odds Report different from the "Exptected Win Pct" on the same report? E.g., Cards expected win pct. is .522 and expected wins is 85.8; Brewers expected win pct. is higher (.524) but expected wins is lower (85.2). Why are the projected win-loss records of individual pitchers so far off from what they should be? Wins are being massively inflated, as has been pointed out by several users. How often and how aggressively are the Depth Charts being updated? (Jason Castro, e.g., is still being projected with 400 PA, ~48 hours after a season-ending injury). All of these questions are not new; rather, they have been raised repeatedly over the past several weeks by a wide range of users. I have yet to hear a definitive answer from anyone at BP -- either via customer service email, or via comments.

I would be interested to know what exactly the W-L projection algorithm is. Clearly, it's not even close to working properly (e.g., LA Angels). Commenters have pointed this out on individual team pages since as far back as Feb 21, but no acknowledgement or fix from BP as of yet.

Colin only says, "Due to unbalanced schedules, we come up with win-loss records that diverge a bit from the numbers on the depth charts." But this does not make much sense, since both systems should be using actual schedules as far as I know. If not, what exactly do they each use for schedules?

There is going to be some rounding error, esp. with the middle relievers. But yes, there is obviously a problem beyond rounding going on. Not just here, but with most teams. No acknowledgement from BP yet.

This says it was updated at 2:24pm on Mar 4. Castro was diagnosed with a torn ACL at 8:00am on Mar 4, yet is still showing with 400 projected PA. Certainly I am not saying that playing time adjustments need to be made intraday, but I am saying that when you do make an update, every effort should be made to include the latest injury info. as of that time...

"Jason Kubel needs to be something more than the guy he's looked like outside of the abandoned Metrodome, more like the hitter he was in 2009; a career .257/.330/.449 performance on the road suggests that, highlights aside, there's only adequacy on tap." What does this mean? His career home/road splits are well within the range of normal: .819 OPS at home, and .779 on the road.

Would be nice to have a single page that lists which teams' depth charts were adjusted on a given day. Right now, the only way to see which teams have been updated recently is to go to every team's page and look at the time stamp.

Do you really think Wieters is a top 50 failure, or are you just reaching for attention? (I'm not sure which would be more damaging to your credibility).

The player cards with 10-year projections as well... No ETA, no mention from anyone at BP in over two weeks. I think a status update is in order.

I don't really get the point of this article. Bullpens are important. Given that bullpens pitch almost 1/3 of all innings, that's pretty intuitive. I'm just not sure what else one was supposed to glean from this article.

Do you have data showing that a ~5-10 degree drop in temp. causes a significant change in pitch velocity?

Love the article. Looking forward to more. tbwhite raises some really good points that make the causality difficult to establish, but I would imagine that extreme cases (over several seasons) are of value in determining which pitchers likely have unusual fatigue patterns.

Can you post a note on http://pfm.baseballprospectus.com/updates.php each time you update playing time projections? Right now, it's unclear, e.g., if any updates have been made since the initial release on Feb 16.

This is incorrect. All the traditional stats projected by PECOTA are done so factoring in the player's park.

Exactly. Catcher development is highly unpredictable. It's embarrassing (for the entire BP staff) to read Wieters' name in this list. In additon to the aforementioned Varitek, Victor Martinez and Jorge Posada were similar "busts" at age 25. Based on the "irredeemably perfect" editing of the 2011 BP annual...I find it spectacularly unlikely that Goldman will be running the show at BP three years from now.

Ah, I see. Valid point. I hope Colin can provide more detail on what is going on.

Can Colin (or anyone at BP) please reply with an explanation of how/why the depth charts use a different schedule projection than the PS Odds? Specifically, what is "unbalanced schedules" referring to?

I have asked this question here because I have received no answer from customer service (or anywhere else on the site). And yes, long-term projections are very relevant to the topic of evaluating subjective top prospect lists.

Player cards release date?

Well, you need to assume something about the schedule in order to make a runs scored/allowed projection -- if it's actually a "projection" and not a simple power rating. Given that their header (on http://www.baseballprospectus.com/fantasy/dc/) is "American League, ranked by projected 2011 record," I would have assumed it uses actual schedules as a factor. But now I have no idea. Relatedly, are player projections using actual schedules, or are they assuming a league-average schedule? I sure hope it's the former -- or at very least a division-average schedule. Would not be much point in creating advanced fantasy projections if you didn't take this step.

I think you are misreading the table. It says that the Brewers division odds have increased from 27.0% to 32.6%.

"Due to unbalanced schedules, we come up with win-loss records that diverge a bit from the numbers on the depth charts." I don't follow this. What schedule assumptions are you making for the Depth Charts win-loss projections?

Please keep the "last update" date accurate.

...and the Jays have outperformed in all but one of the past two seasons (BP projected 81.2 wins in 2009 when they won 75). I hardly think the system is magically biased against the Jays.

Last Update still says "02-16," but clearly Wainwright was removed well after that date. Please keep the last update date accurate, so we know what news (esp. when it may be more subtle) has already been incorporated.

"You don't put up those kind of numbers with severe splits" Right. The majors are littered with LHPs with very good overall numbers and huge splits. Do some research; check it out. I.e., there is a reason quality LH relievers only rarely become closers.

Thanks. What source do you use? Minorleaguesplits.com has shut down, and milb.com has only the player's most recent team, so I am in search of an alternative.

Wow. Pineda just ranked as #16 in all of MLB at BA, yet not in your top 15 for the Mariners alone.. No idea who is right, but this might be the biggest prospect opinion disparity between BP and BA that I have ever seen.

Any idea what McGee's L/R splits are like from the minors? Unless they are unusually balanced, he would seem to be a longshot to become a closer.

I think TAv would be a nice addition to the depth charts. I realize you cannot add every stat (in the interest of clarity), but since TAv is a cornerstone of BP projections, it should be listed...

Agreed. There needs to be a dated "Change List" on every team page listing what was adjusted when.

Your single-season sample of NHL goal scoring doesn't "empirically contradict" anything... I can't believe I just read your conclusion that "In terms of raw goals, 75 percent of HFA (15 goals out of 20) comes in even-strength situations. That empirically contradicts the book’s assertion that it’s all penalty calls." So a sample of 20 goals in a single season is conclusive? It shows nothing.

ETA for player cards?

Yes, please.

I agree. To me, the article is basically saying, "here is what his career would look like IF he stays really healthy and really good... Guess what? Under those assumptions, his value would be really high." I don't see the value of this.

Nor was Alfonso Soriano...

Please post any important updates to your products in an appropriate, consistent, easy-to-find location. Buried within the comments within a blog post is not the proper location for such information.

How about putting important product release information where it belongs -- on the home page and on the Fantasy page? Why is every announcement relegated to BP Blogs (beta)? Announcements are not blog content, and for that matter, how is a blog in "beta?"

What "cliff" are they falling off of? Stanton's OPS is projected to decline by 13 pts; Heyward by 49. Heyward had an extremely lucky year by any accepted metric of luck. .335 BABIP (top 20 in MLB) and 16.8% HR/FB (top 20 in MLB). Both are likely to regress.

Wholeheartedly agree. There needs to be an easy way to look at each player's offensive value.

"Discussions" implies that there was some back-and-forth. If it was truly unanimous, what exactly were the discussions about? Perhaps the pros and cons of publishing non-baseball commentary? Pros: Mildly amusing half of your subscribers in a forgettable way. Cons: Irritating/pissing off the other half in a memorable way.

Well said. As far as I can tell (and I am about as liberal as one can get), this is BP's cry for help.

Or...just keep non-baseball articles off the site -- and yes, politics aside, this is a non-baseball article. Saves countless subscribers time, and presumable saves some resources for BP.

Oh, my fault. Sorry for not loving articles *about* gay porn. Or not thinking it's an awesome use of limited resources by BP...

I don't know you and certainly haven't called you anything. I am pointing out the long-standing pattern of missed deadlines/promises/indications/randomly-generated-dates that has plagued BP for years now. I would love to think they have acknowledged and fixed the core problem. I have yet to see an acknowledgement, and if this particular instance is any indication, the issues remain to be fixed. "Winning" would include: acknowledging the causes of issues in the past, detailing what has been done to fix them, and then delivering products on time.

So should we grant RBI to players who hit a HR just after a teammate gets picked off? After all, the hitter didn't deserve to get deprived of the RBI. All baseball stats are flawed. Not sure I follow the level of agitation with ERA. If "stat reform" is the platform, I think wins, losses and saves deserve attention before ERA.

Are the full 2011 projected standings posted anywhere yet?

I'd agree that it seems like the catcher position has an abnormally high flameout rate, but according to: http://www.royalsreview.com/2011/2/14/1992424/success-and-failure-rates-of-top-mlb-prospects it looks fairly normal.

It's not the two days that even matters. It's the blatant spinning of this as some sort of evidence of BP's commitment to excellence that leaves a bad taste. I don't think even Microsoft would dare use "Irredeemable Perfectionists" as the title of what amounts to a bug report and release delay.

Well said. The next time I postpone something on the very day I had promised it, I am going to avoid using the words "apologize" and "sorry" -- opting instead for "irredeemable perfectionist." I might throw in a few really obscure, off-topic references just to add to the smokescreen effect. Presumably, you've been working on this for 10 months now. To let it get down to the last day like this is just poor planning. There have been major missed deadlines for each of the past five years. To continue the [bizarre] alcoholism parallel that Goldman leads with...the first step to fixing a problem is admitting you have it. The "problem" in this case is not an individual error or technical issue; it is a poor process for planning, managing, and controlling quality.

Not sure I follow: are you saying that a one-year change in flyball rate is less predictive of a persisting change than a one-year change in HR/FB rate? I am concluding this from "his HR/FB rate was a not-terrible 10.2 percent last season" and "Prior to 2010, he had a pretty consistent fly ball heavy batted ball profile with the Marlins, and I suspect we'll see that again in 2011." I would think a change in flyball rate is more alarming (for one season) than a change in HR/FB rate, since the latter tends to fluctuate a lot y2y regardless of the player.

"Gassed," or did he just draw the Red Sox/Yanks for three of his six Sept. starts? Looking at his games log, it would appear to be the latter. His avg. FB velocity also supports the notion he was not "gassed" in Sep., as his highest avg. velocity was actually recorded in his final outing of the year. http://www.fangraphs.com/pitchfxo.aspx?playerid=3340&position=P&pitch=FA

Single season home/road splits are not even close to being predictive. Thinking you are smarter than you are is how you lose in fantasy baseball...

Do you have aggregate data on this? Care to publish a study on it? "Crushed" means statistically significantly different than. Chone, for example, has shown itself to be superior to Marcel. But according to you, they (the forecasts) are all the same in avg. quality, and there is no hope of outperforming consistently. Contradicting facts aside, even if that was the case, why would you (BP) build/re-vamp a very complex projection system, when you know the work will not result in any consistent outperformance over a simple past-years' stat average system? Just for kicks? BP really can't have it both ways. The stance needs to be one of the following: (1) We are committed to producing the most accurate baseball projection system possible. We have worked tirelessly on possible methods of attacking this problem and believe ours is the best. We plan to charge money for these projections (alone or as part of an editorial package), and will in return provide objective evidence of our progress -- both in backtested results and in real-time (end of season) performance measurement, vs. various other methods/systems. (2) We have looked at the issue of how best to project player stats and have concluded that there is *no* benefit to doing anything more than averaging the players' past stats (Marcel). We believe that Marcel is and will always be about as good as the very best projection system, so we have formally given up. We will no longer be charging for fantasy projections and will refer all of past customers to a calculator. Obviously, stance #1 is preferable, but #2 would be fine as well. But trying to have both at once is....a problem.

Marcel most certainly is not "among the best." This has been proven over and over. CHONE has crushed Marcel over the years, and PECOTA used to until 2009. My question remains: is this (the spreadsheet in this post) some kind of stripped-down pre-PECOTA algorithm, or is it the final algorithm you plan to use for the pre-season 2011 projections?

I, too, do not understand how/why Marcel is anywhere in this article. The goal is be accurate, not just "no worse than the worst projection system out there." If your model is not ready for release yet, then don't release it. People can get/generate Marcel projections easily enough on their own, if they need something for today.

The goal should be to improve accuracy. Period. Any smart user is well aware of variance. So no, the goal of PECOTA should not be to explain what variance is; it should be to improve forecast quality (average error).

I don't get this (below). Are you saying you are currently using some algorithm that is different from the one you will use to make final forecasts? That would make no sense. "This is the first release of PECOTA, and as such will continue to undergo revisions through the remainder of the offseason. The program we use to generate the PECOTAs is continually evolving, and when we discover new ways to improve the forecasts, we'll make those changes and pass the updated forecasts on to you. We’ll also be updating periodically to keep up with players who switch teams."

Can you post, or point me to, a summary of what has been changed in the PECOTA algorithm for this season?

How about the top int'l prospects who have not yet been signed by a MLB team?

Why don't you create and test a "predictive FIP" then? "2. FIP is not meant to be predictive! FIP merely represents current performance. In no way should one even think that you would regress K rates the same as HR rates. If I wanted a "predictive FIP", I would probably do something like (5*HR + 2*BB - 2*SO)/PA + constant or something."

Nice job. I think the next step is to look at which estimator performs best for in-season projections. That is, compare the first-half ERA estimator with the second-half actual ERA (filtering out team changers and guys with not enough IP in both halves), and assess the average errors.

I hope you can provide detail at some point about the process you plan to use to generate in-season projections. That is, how much to weight current season stats vs. pre-season projection in coming up with a rest-of-season projection. Also, would love to see any work you have done to test how well the current PECOTA algorithm has performed in backtesting -- similar to what Swartz did in his article about SIERA.

I do not think proper resources are being directed to developing a valuation engine (it is not a trivial process), nor to updating playing time projections as accurately as possible. There is no quantitative way to project playing time; it requires significant expertise and time. I would think this expertise exists somewhere on the staff, but not convinced it is being applied fully at this time.

...and there is no point is using a projection system as an input unless it is expected to be one of the very best available. A state-of-the-art algorithm from five years ago, awkwardly dragged through several obsolete/inappropriate technologies while putting little effort in actually improving the algorithm, is not necessarily one of the best available inputs in 2011. That said, I look forward to future installments of this series to see if sincere and thorough efforts to improve the system are in process.

More detail on pitchers' average fastball velocities. Less fluff (replace "Ephemera" section with useful content).