Kill the Win

Dan Straily has been a pleasant surprise, but could be in for regression

With a pitching staff decimated by injuries, the Reds have had to use 10 different starters this season, some of whom were completely unknown to most fans before the season started.

Only three pitchers have started more than 10 games, Brandon Finnegan being the only one who was actually projected to be in the rotation before the season began. The other two are Alfredo Simon and Dan Straily.

Simon, coming off a terrible season with the Detroit Tigers, has been even worse this season, and would have been long gone by now without the injuries.

It’s been a different story for Straily. Tossed aside by the Houston Astros and San Diego Padres in spring training, the Reds swooped in and claimed him on waivers. The right-hander has ended up being the team’s most reliable starter to this point.

Straily currently leads Reds starters in ERA (3.34) and fWAR (0.6), having only given up more than three runs one time, which can be forgiven since it came in Coors Field. Aside from Raisel Iglesias (23.6 percent), he is the only member of the rotation to strike out more than 20 percent of the hitters he’s faced (21.1 percent). He has also been the most consistent starter when it comes to throwing deep into games and sparing the bullpen, averaging just under six innings per start — with seven of his last eight outings going six innings or more.

All in all, the organization couldn’t have asked for much more from a last-minute waiver wire addition. Along with Finnegan, Straily has brought some much-needed stability to the rotation.

But could he be due for some regression? There are certainly some signs pointing in that direction.

Despite Straily’s tidy ERA, estimators indicate he hasn’t truly pitched to that level, with each being at least a full run higher than his actual ERA (4.43 FIP, 4.61 xFIP, 4.47 SIERA).

(As always, if you’re unfamiliar with these stats or need a refresher, feel free to give this piece by Steve Mancuso a read.)

Why don’t Straily’s numbers match up?

When it comes to xFIP, it has a bit to do with having home-run-to-fly-ball ratio (11.3 percent) below league average (12.5 percent), so there’s a slight adjustment upward for that. Largely, however, these inflated numbers boil down to having control that escapes him at times. Among 107 qualified starters, Straily has the 12th-worst walk rate (10.5 percent) and is one of just 16 pitchers to have a BB% north of 10 percent. He is also tied for sixth in baseball with five hit batters. When a pitcher walks that many hitters and has a good-but-not-great strikeout rate, ERA estimators typically aren’t too kind.

There are other signs that Straily’s results aren’t painting the most accurate picture about how he’s pitched.

Firstly, his .223 BABIP is quite a bit lower than his career average of .257. There could be a couple of explanations for this:

1.) He’s allowing fewer hard-hit balls than usual, resulting in weaker contact and more outs.

OR

2.) There’s a bit of good fortune involved (i.e., a lot of hard-hit batted balls are going right to his fielders).

It appears to be more the latter than the former for Straily. Here’s a look at his line drive and hard-hit rates in 2016, as compared with his career averages:

straily hard contact rates

In spite of the good results Straily has received, these numbers show that his BABIP will more than likely regress to the mean eventually, and will almost certainly bring along a higher ERA with it if his strikeout and walk rates stay in their current vicinity.

There is one more area that correlates with BABIP and gives further evidence as to why regression could be on the way for Straily: strand rate.

If you’ve watched some of Straily’s starts this year, you’ll notice he’s been particularly good at getting out of jams. Currently, he’s leaving 80.2 percent of runners on base without allowing them to score, good for 18th in MLB. That’s obviously a good thing, but a percentage that high typically cannot be maintained, especially for a pitcher without an extraordinary strikeout rate. There are exceptions to this, like Zack Greinke last season, who had a 86.5 LOB% and a 23.7 K%. However, in most case, strand rate tends to even out over time and trend more toward the league average, which has ranged from 70 to 74 percent over the last 50 years.

These underlying numbers don’t indicate that Straily has pitched poorly by any means and shouldn’t take take away from his performance this year (after all, an ERA in the range of 4.50 would be better a better mark than five of the other nine Reds starters have put up this year). He has undoubtedly been a welcomed addition and pleasant surprise for the Reds. However, there are several reasons to expect some decline from him as the season rolls on.

43 thoughts on “Dan Straily has been a pleasant surprise, but could be in for regression

  1. Straily has been the MVP of the pitching staff. He helped hold together a patchwork rotation through the dark days of April and early May when it was crumbling apart. When Straily was released by the Padres, I knew he would be a good pickup for the Reds, but I didn’t know he would do this well. Eventually he will end up in the bullpen. I hope he helps the bullpen as much as he has with the rotation so far in the first half of the season. Solid, even if unspectacular.

  2. Thanks for bursting the bubble of fun on Straily. Then again stats can be made for or against this guy it all depends on if you are a half empty glass or a half full glass kind of a person. This coming from someone who is almost always a glass is completely empty if it has water and not rum n coke mix. 🙂

    • “Is it half-full or half-empty?”

      “Who cares! Where is the booze!”

      Good stuff.

  3. There’s no compelling reason or argument that suggests Strailey’s BABIP should start to regress this year to some established.mean. Your data is not predictive, and to suggest otherwise is disingenuous. No saber data is predictive. ERA and BA aren’t either, for that matter.

    In cases such as Straily, he could be having a great year out of nowhere. He could have improved his pitching skills due to hard work and excellent coaching. He could be the beneficiary of a better defense behind him than in the past. He could be more comfortable in Cincy and therefore sleeping better. My Gosh, there are hundreds of viable reasons far outside of saber numbers that would better explain his great start to the year.

    We tend to evaluate player performances on a yearly basis, but players exist outside the annual calendar., if you know what I mean. They could get hot in August and stay hot into June 2 years later. Means based on the yearly calendar are nice, but are definitely not predictive.

    If you had asked me what I thought of Strailey, I would have said; ” He looks pretty damn good so far, but maybe he’s pitching a little over his head. Who knows? Let’s hope he’s turned a corner and it’s no illusion. Just have to wait and see.” I could give that answer in a minute or two, and it’s every bit as accurate, maybe more so, than anything a deep saber analysis can come up with. I’m appreciative of the work you put in, because it’s interesting of itself, but please remember not to make predictions with it. Those have little basis in reality. Every day becomes the past a day later, and yet life is never logical.

    • I dunno, Jim. I get from this comment and others you’ve made that you aren’t a fan of “saber data” or advance statistics. That’s fine if it’s your opinion. But I wouldn’t state it as fact, as you’ve done, that FIP and xFIP aren’t predictive tools. That’s exactly what these measures are designed for. And, really, if you think about it, there are some common sense principals behind them. A pitcher that walks a lot of batters isn’t likely to have much success for long, etc., etc.

      I think it’s a bit disingenuous of you to suggest that this writer is stating as fact that the FIP and xFIP guarantee that Straily will start to decline. He rather clearly suggested that these measures are indicating he might. Big difference.

      I like a good argument as much as anyone else. But I don’t see any value in making them up.

      • Here’s a quote from the above article. I could pick out a couple others if I wanted to.

        “these numbers show that his BABIP will more than likely regress to the mean eventually, and will almost certainly bring along a higher ERA with it if his strikeout and walk rates stay in their current vicinity”

        BABIP does not regress to a mean. That is a false statement. Most saberists certainly agree. Do you know what the Bell Curve is? Well, the high point is where sabermetrics live, at the average point. But lots of people live out their entire lives out on either side of it, at various points. Some people spend much to all of their time down at the flattened ends, even. Their lives never fit averages. That’s an oversimplified example, but hopefully you get the picture.The average is based on all sort of different events, periods, micro and macro time frames. It only tells you the midpoint and the time frame is an arbitrary. It is not a calculus, where tries to tell us the exact point a function (event) is occurring at any exact moment in time as it moves thru time and space.

        You do not understand sabermetrics and averages if you think I’m the one making things up. I’m merely correcting distortions that pop up from time to time. Sabermetrics did not, could not predict Votto’s year so far, nor Bruce, Straily’s, or anybody else’s. Using it as a visionary tool is using it incorrectly. The past does not predict the present. We only get suggested tendencies, not likelihoods. Please don’t attack me for simply being honest, or call me disingenuous until you understand these things better..

        • Unlike hitters, who have distinctive batted ball profiles that can produce variable BABIPs, pitchers face the same mix of batters. They have little control over where the ball goes when it’s hit. So pitcher BABIP does regress to a mean. League average is 29% A few pitchers, like Clayton Kershaw, might be able to sustain a BABIP a little bit below, like 27%. But that’s about it. There’s a narrow Bell Curve, but it functions between 27-30%. Dan Straily won’t continue a BABIP of 22%. Matt’s statement is absolutely right. The only one attacking people for being honest just because he doesn’t understand this concept better is you.

        • Steve was kind enough to entertain your false narrative. No one said Straily or anyone else will, with absolute certainty, regress to the mean. He said more than likely he will, eventually. Again, big difference to what you are suggesting. We can all acknowledge what the word “mean” means.

          Remember how I said you’re making up arguments? You’re proving my point.

          If you perceive my comments as an attack, then perhaps you should consider the tone you are taking.

          Matt presented a nice, informative, article on some measures that suggest that Straily’s nice start may not sustain itself. You or I could disagree on his conclusions. But it is you who suggested that he was being disingenuous. How is that being constructive?

      • This pretty much hits it on the nose, MrRed.

        I’m in no way saying “Dan Straily will absolutely, 100% regress because these stats say so.”

        I’m making an argument, based on these sabermetrics, that there are some warning signs that he might see a dip in production. That’s it. I wasn’t trying to make any definitive statements or take anything out of context just to prove my point.

        • You basically did exactly what an analyst should do. You’ve presented a case that suggests that Straily may see some regression. A scout may, looking at how Straily pitched in the past and how he isn’t really doing a whole lot that’s different this year, come to the same conclusion. Good stuff here Matt. I tend to think we’ll see some regression out of Straily based on your data and the fact that from a scouting standpoint, I pretty much see the same pitcher I’ve always seen. Also, people need to understand that when you say “regress”, it doesn’t mean “gonna stink to high heaven”. It means that his end-results may not be quite so good.

    • “No saber data is predictive” is true only if you mean perfectly predictive. “Saber data” is just data. Some data is quite predictive, given adequate sample size. The number of home runs hit by a batter one year is a better prediction of the home runs he will hit the next year than just randomly guessing. It isn’t perfectly predictive, but it has a certain level of prediction. There are coefficients that show the relationship and they aren’t 1 but they all aren’t 0 either. So you’re oversimplifying in grouping the predictive power of all statistics together and just flat wrong in saying none have predictive power.

      While it’s true that FIP and xFIP weren’t designed to predict ERA, studies show that they do a better job of predicting future ERA than does past ERA.

      In terms of BABIP, there are mountains of research showing that a pitcher’s BABIP will move toward the league average (.295) over the course of a season. They don’t all end up at .295 because sometimes luck doesn’t even out over the course of a season. But Dan Straily isn’t a 22% BABIP pitcher. When pitchers have “great” years, it’s important to tease out the talent part and the luck part.

      We went through this same stuff when Alfredo Simon started off 2014 with a great BABIP (.232) but finished with a second half of .309.

      • Not to side with JIMMALONEY, because I don’t agree with what he’s saying, but there are certainly ways to run a consistent lower-than-average BABIP against. With the implementation of StatCast, people are starting to delve into the realm of “contact management.” Some pitchers, because of the way their pitches moves and how they employ the pitches, can create less authoritative contact than others, and thus, a lower BABIP.

        In the modern era, here are a few outlier-type examples in a statistically significant sample size (2000+ innings pitched).

        Catfish Hunter, .242 BABIP, 3300 IP
        Jim Palmer, .248 BABIP, 3800 IP
        Tom Seaver, .259 BABIP, 4800 IP
        Nolan Ryan, .265 BABIP, 5400 IP
        Barry Zito, .273 BABIP, 2600 IP

        If you look at the names on this list, they were stars in their time, one and all.

        Agreed that guys without plus-stuff aren’t likely to deviate much from .295-ish (and that Strailey’s number is complete lunacy), but pitchers do have control, to an extent, over their BABIP.

        But anything under .280 should be HEAVILY scrutinized. The standard deviation is about 12 points. So .283 is 1 STDEV lower than the mean, .271 is 2 STDEV below the mean, and .259 is 3 STDEV below the mean.

        From 1920 to now there have been 264 pitchers who have thrown 2000+ innings. Survivorship bias tells us that each of these 264 pitchers were “good.” At least good enough to warrant being a starter for 10+ years. Even in this sample, there are only 9 guys EVER to hit that 3 STDEV below the mean BABIP of .259. Six of the 9 are HOFers, 2 of the remaining 3 are borderline sort of guys.

        So, to say that there is no compelling reason or argument to show Straily’s BABIP is coming up is one of the most inaccurate statements that can be made. Dan Straily is not the best pitcher in baseball history by a wide margin. Because if you think he can maintain a sub-.240 BABIP, then that’s what you’re saying.

        • Patrick, I know you are a math guy, and have already been giving your research kudos. Perhaps you haven’t notice. Look, I enjoy all the research everyone does here. I just want to remind folks not to spend a lot of time trying to definitely prove the obvious, which is that we don’t really know what’s going to happen next, we can only discuss it and guess.

        • Sorry about the out of date article. It has a timeline of June 8, 2016 on the top of it. I guess it’s the page date, not the article date. Sometimes pages bring up old articles and put them at the top of the current page, probably so after you’ve read it, you might stay and read the current ones. My mistake.

      • Steve, I agree with your second last paragraph 110%. Hardball times just ran an article today, evaluating 7 different BABIP formulas. Check it out. This stuff is similar to beta right now. BABIP may very well be using a very different formula in a couple years, making today’s BABIP obsolete and maybe even a poor way to evaluate.
        http://www.hardballtimes.com/whats-the-best-babip-estimator/

        I disagee somewhat with a few of your other points, but that can be for another time. I hope you noticed I said the data is suggestive, but not predictive to the point teams could really rely on it. The If/when as whether Straily comes back to the top of the Bell Curve is something no one really knows for sure.

        What’s problemetical for me, and you alluded to why, is that the actual percentage of predictiveness is not included in so many analyses I see here. Too many times, I’m seeing advanced data guys present their data with terms such as “likely”, “probably”, “should”, while implying 80-100%. When the weatherman sez it’s an 80% chance of rain, I don’t set up a tee time. When it’s 20%, I do. Either the data is’t that predictive/accurate yet, or they need to start including such. 0-99% is a huge range of uncertainty. And, on top of that, even the weathermen give us 95% sometimes and are dead wrong. I put Votto, Bruce, even Cozart, Hoover, Straily in that category. That’s a lot of guys way off their averages to be singing the praises of any data built around averages. My point, having played a bit, is that baseball is so unpredictable and full of mystery no number system will ever predict all the surprises that happen and upset the percentages.

        It seems ironic that those into the new methodologies go deep into sublevels of esoteric data, which we need to remember will often diminishing returns due to its very narrow subset range, yet they present no exact percentages of the predictive strength/weakness of their findings per each extraction. Exact percentages would seem right up their alley, and yet I read vague terms like those above, some slanted to make their point seem smarter. Hmmm, that’s not cool. Either you’re trying to be more accurate or not. Right? Oh, well.

        Some commenters here have asked me to continue raising questions as I see them. They said they felt unwelcome when they did so, and hoped I wouldn’t give up. Well, I welcome open discussions, but have no interest in dealing with snark, which I’ve already gotten, including today (not from you). Some in the saber crowd have a rep for that and bullying. There’s no place for that in any civil discussion. Snark is a form of immaturity. It hurts the saber “movement”.

        BTW, I minored in math and played baseball in college. Took calculus at 15. I know advanced math. I’m confident I have cred behind my words. I also know I’m not right all the time. Saber is, like I said, in beta. Heck, different teams use different forms of WAR. You saw my ref to all the BABIPs above. I’m trying not to upset the apple cart, just adding some important points. Apparently that can you barked at pretty quick. I fail to see any sound reason for so much misplaced sensitivity.

        • Jim, I think your initial assertion that there was no credible reason to think Straily’s BABIP would go up is probably what spawned most of this.

          Don’t use aggressive rhetoric like that without expecting a bunch of long replies! 😉

          Also, I majored in math and am currently employed in a management position over rocket scientists. So I hope I have cred behind my words, as well.

        • Sorry, you said “compelling,” not “credible.” I got my “c” words mixed up.

        • Also, your BABIP article linked is from 2009. We’re way past that point in estimation, and some folks are already rolling in StatCast granular batted ball data, as available.

        • You’re the one who took Matt’s original statement, which was loaded with conditional terms: “could” “more than likely” “reasons to expect some decline” and paint that as though he said something would be 100% likely. In fact, you’re the one quoting absolutes about how there is NO predictive value in statistics. So I’m not buying your new configuration of being against certainty.

          You’re also conflating the idea that we can’t know everything with that we can’t know anything. If there is uncertainty, then there is nothing. That’s essentially your point. And it’s obviously wrong.

          “Deep sublevels of esoteric data” – please. There’s nothing esoteric about saying that strikeouts and walks are the best measure for pitcher performance. You might disagree, but there’s nothing esoteric. Or nothing intentionally deceptive “to make their point seem smarter.” If anyone is guilty of that in this conversation, it’s you as I pointed out above.

          Your grouping of every baseball statistic as “saber” belies your understanding of this and your ability to apply whatever background you have to thinking about this. Either that, or you are deliberately trying to be misleading.

          Look, you can’t come here and assert that you’re smarter than everyone in the room with great certainty and then be offended when people challenge you aggressively. I hardly ever respond to comments that I think are wrong. But I do (when I have the time) when someone takes your tone of certitude about something he/she obviously doesn’t understand. The article you cited as “just ran today” is not only from 2009, it’s about HITTERS BABIP, which isn’t relevant to our discussion of whether Straily’s BABIP will likely regress to the mean. Further proof you don’t know what you’re talking about. Just a bunch of words.

    • +1 Jim. Love it. Stats cannot predict the rain nor the best hitter or pitcher on a team. If gives people something to do other than kill themselves I suppose, but other than that I think that scouts use all stats as just a small part of the entire measuring process for any player.

  4. How much do you think that the differences in the defensive alignments behind him (vs. earlier in his career…since you are comparing LD% and Hard% from this year to his career numbers) have had to do with any of this?

    • I’m not sure if you’re asking me or the author, so you can ignore my answer if you want his. (I mentioned defense in my first reply). I think with Hamilton in center, and with Cozart having a tremendous year defensively, the Reds are giving Straily a strong defense. Yes, I know Suarez exists, and the Reds are committing some dumb errors, but Bruce, Phillips, and Votto are still way above average most of the time. Sometimes pitchers pitch to contact more confidently if they trust their defense better, and it can be symbiotic.

    • Defensive capability and shifts can have a large effect on ERA, although you would expect to see it across the pitching staff, not just one pitcher.

      Shifting has less league-wide impact on BABIP than you would think. Research shows a shift lowers batting average by about 35% when deployed (from .265 to .230) for ground balls and short liners. But shifts are mainly used for LH batters and not every one. A 10% use rate would lower league BABIP by 3.5%.

      That will go up as shifts are used more against RH hitters and against more LH hitters. Although if hitters adjust, it will go down. League-wide BABIP this year is .294 which is in line with an average year.

      But it’s unlikely to explain Straily’s BABIP being 22%, that’s just too much variance to explain.

      As to whether the Reds defense is making a difference, there is no reason to guess about it. The 2016 Reds rank #11 out of 30 teams in turning hit balls into outs. The Reds convert 71.1% of hit balls into out. The league median is 70.7%, so the Reds are right around average.

      The statistics of FIP, xFIP and SIERA were designed to take the defensive aspect out of it and focus on what the pitcher controls – K, BB and GB%. They’ve turned out to be better predictors for future ERA.

      This area will develop in the future as we figure out how, if at all, the new StatCast data can be used to further refine it. The first step is determining to what extent a pitcher controls the average batted ball velocity and trajectory. That work is at the starting point because of sample sizes.

      • I wouldn’t expect to see the shifting effect spread too evenly across the entire pitching staff (due to handedness, a pitcher’s pitch mix, etc.).

        Straily faces more lefties than righties and the Reds are shifting a lot more for him this year than the A’s did back in 2012-2014 (in aggregate, at least). Batters are also pulling the ball more against Straily (which plays at least somewhat nicely with the shifts) and hitting fewer fly balls against him than they did earlier in his MLB career.

        Dan is in the top ten in the bigs in “soft” (<90 MPH) line drive allowed but not in the top thirty in "hard" ones.

        It's interesting to see that Finnegan and Straily are first and second in MLB in lineouts so far this year….

  5. Straily has reminded me of Sammy LeCure from day 1 with the Reds. He’s not very big or athletic and his stuff is marginal but he keeps the ball out of the middle of the plate and changes speeds. They’re both even better vs lefties w/the changeup. Straily might be a 5th starter type that can put up a Leake era of 3.90 or whatever without eating as many innings but I seem him as more of a long man or come in a tough spot in the 6th or 7th like Sammy used to do and limit the damage. We’re seeing how having a bunch of chuckers in the pen is working out? Trying to throw each pitch harder then the one before is not a strategy?

  6. The comments are getting to jumbled up top, so I’ll post here…

    Regarding the predictive power of statistics…

    The method to determine “predictive” power is season-to-season correlation.

    For the pitching stats, here are the year-to-year correlations between RE24 (the best measure of the effect of your at-bats against) and the reference statistic, based on all pitchers who faced at least 170 batters between 2011 and 2014:

    cFIP: -0.40
    SIERA: -0.38
    kwERA: -0.38
    xFIP-: -0.35
    FIP-: -0.34
    xFIP: -0.34
    FIP: -0.32
    ERA-: -0.28
    RA9: -0.27
    ERA: -0.27

    As you can see, no single stat is perfectly predictive, to use Steve’s term from above, but some are more predictive than others.

    For example, if a guy had a 4.00 cFIP/SIERA/xFIP/etc… you’d be correct more often saying he’d be a 4.00 ERA guy next year, than if he had a 3.50 ERA and you said he’d be a 3.50 ERA guy next year.

    Source: http://www.hardballtimes.com/fip-in-context/

    • You may find this ironic, but that’s exactly the point I was trying to make. Some of the new stats are proving to be very useful, others less so, because they are so far showing a higher degree of predictive accuracy. Just like the weather forecast. 90% chance of rain is far more definitive than 50% chance. My point all along is there is a gaping hole in the new metrics in efforts to produce actual percentages we can see relative to each analysis, such as Straily today. Straily has such a small sample point pitching as a Red in GASP and in the NL it’s way too early to suggest predictiveness. I still stand behind that point. Not to mention things don’t always go as predicted, of course. That was my other point. But 0-99% is way too big a range for me to have to guess at, when you’re making a prediction. OK?

      I earlier called those stats with the wider variance of accuracy “suggestive”. Actual percentages would be better and clearer for comparative purposes. Apparently everybody missed me saying that. Saber is very similar, in my mind, to the initial oddmakers at the race track. Their job is to set the odds. They do research based on all their data, mixed with personal knowledge. I think it would be very interesting if betting were to become legal in baseball, and people could bet on the game up to when it started, which would lead to dynamic odds changing all the time, just like at the track. Saber might grow exponentially if that happened, as tracks would want to make even more sure the initial odds were still slightly in their favor.

      • My post wasn’t meant to refute anything above, just to add additional information. 😉

      • As a gambler….we would stop sports betting if they changed it to the paramutual ripoff style of horseracing! The whole point is to look for value….for instance the Reds were +185 vs Strasburg/Nats the other day which means a $100 wager returned $285. Getting stuck with a bad line at first pitch like the horses would defeat the purpose!

  7. I have respect for those who do, but I do not have a real interest in advanced statistics. And since I don’t have a position that would influence the comings and goings of my favorite team where sabermetrics are important, I will continue to enjoy baseball for the great art that it is.

    • Certainly nothing wrong with that! We all fell in love with baseball for that very reason… it is living, moving art.

  8. There are two Reds articles on FG today. If folks haven’t read already, might as well!

    One, although not sharing the premise, is very similar in presentation to what Jason Linden wrote in his “Trade Adam Duvall” piece the other day.

    The other article is about how bad the Reds have been, but how it isn’t so bad because the Reds have (should have?) learned some stuff.

  9. Folks the comments ended up being over my old head. The advanced numbers are a lot like the old numbers of using ERA and BA against in that by the time they get to be “absolute” the player is in decline. This article and the equations reinforce what my eyes see as much as he has helped this lack of staff. He gives up laser shots that so far keep finding a glove. I don’t think what measuring stick you use there is a very good chance the tide will turn.

  10. Someone asked Reggie Jackson about Nolan Ryan and he said something to the effect of “Everyone likes fastballs like everyone likes ice cream but you don’t want it fed to you by the gallon”. I feel that way about this site. I love statistics but some of this stuff is way too much for me?

    Anyway….I still have the same argument on BABIP that I’ve used 100 times. I read on here literally every day how unlucky Cozart was a few years with his BABIP. It was not luck!! His swing was flawed and he was popping up twice a night atleast? The same thing probably applies to Straily’s BABIP! Not you narrow that down to line-drive rate by a hitter or LDR allowed by a pitcher then I’m totally on board!

    • Two of the regular columns on this site specifically exist to use “advanced” metrics in player evaluation; Matt’s “Kill the Win” and my “Fridays Above Replacement.”

      I certainly understand that isn’t for everyone, but that’s why this site offers something like 10-12 original pieces of work per week.

      • Don’t get me wrong….you guys do great work! I love being able to keep up with the Reds….even this year:)

    • +1 funny how luck and unlucky go hand and hand with the most recent sabermetric arguments. What doesn’t measure as success or “predicted” is determined as lucky or unlucky. How convenient. To cry lucky and unlucky when there is no stat to quantify what is going on. I think measuring the value of a players performance based on sabermetrics alone is going to result in either “lucky” or “unlucky” results. Basically the same results as if I didn’t use any statistical measurements at all.

      • So you’re basically saying that there is no value in quantitative analysis when it comes to baseball players?

  11. To me this where I find stat folk well kinda bummers. Who cares what happens, I go to the game and just watch, I enjoy what I see and don’t worry about what might or might not happen down the road. Enjoy the game now, why worry about what could happen.

  12. Steve, sorry if I riled you up. I hope you’re feeling better.

    Actually, thanks for challenging me aggressively. I thought I was supposed to try to keep things calm and civil here, but you’ve enlightened me otherwise. I too enjoy a good challenging discussion and have had a particularly nice one with Patrick Jeter today. But he and I have been very civil with each other, and I hope I didn’t offend you by being pleasant with him. I realize now from your response that challenging someone aggressively means it’s OK to tell them they (me) have no idea what they ( I’m) talking about and it’s all just a bunch of words. Thanks for the clarification.

    I also apologize for knowing higher math. I realize no ex jock should know anything more than PhysEd, especially a mere baseball player, which is why I was always treated like a freak in school, so I accept the criticism happily as justifiiable. I’ve often tried to unlearn it, but with no luck. Damn stuff sticks in the head. I will hereby try to be stupid again. You should be pretty happy with me today, since you’ve pointed out I’m not nearly as smart as you think I think I am. In fact, I’ve been pretty stupid often here, according to your remarks. But then I read a little between the lines, and I guess that was stupid too. If I misread you, well, remember, I’m stupid, no matter what.

    I expect I will continue to say really dumb stuff, like sabermetrics right now seems far more reactionary than visionary to me. I will probably continue to suggest that it may evolve into being visionary someday, imho, when it get percentages on predictions worked out. Yeah, I know, I’m a slow learner and I try your patience. But I hope I still have you pointing out with my dumb thinking to me. Thanks for all your input today, Steve. You’ve been a big help. Oh and I would have sent this straight as a reply to your final comment, but apparently your reply button got turned off. Yeah, it’s completely disappeared off the face of the earth. I’m sure it’s just a coincidence.

    Wishing you the very best, and Go Reds..

    • After a certain number of levels of replies, there aren’t more reply options – that’s a function of the template we use so it works on mobile devices. Yeah, it’s either that or I was terrified of your reply. One of those things.

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