Suppose you “win” when your dice roll is higher than your opponent’s by at least 2 points. So, when your opponent rolls 1, you can roll 3 or higher to win. If he rolls a 2, you need to roll 4 or higher. He rolls 3, you need to roll 5 or 6. If he rolls 4, you need to roll 6. That gives you 27.8% chance of winning. That’s your expected value, that’s your true talent, that’s what we would forecast as your chances of winning.

Now, suppose we don’t know what your opponent rolled. We just know that you rolled a 5. Since you win if your opponent rolls 1, 2, or 3, rolling a 5 is worth “50%”. We therefore estimate that you would have won 50% of the time, if you kept rolling a 5 over and over and over again. What we are doing in this scenario is saying that your opponent’s roll IN THAT INSTANT didn’t matter. We estimate that KNOWING you rolled a 5, we will therefore estimate its value as 50%.

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We know the run values of hits, walks, HR and strikeouts. We can estimate what might have happened if we did not know the sequencing of those events. And we would make that kind of estimate because we strongly suspect that sequencing of events is not a skill, but just a matter of circumstance (for the most part). We therefore KNOW the events, but not the sequencing in making our estimate.

But that is still NOT the same thing as forecasting would could happen. And that is because strikeouts are far more predictive of future events than non-HR hits. Even walks are more predictive than non-HR hits. And this is true to the point that the value of a walk is about the same as the value of a non-HR hit, in forecasting future runs. In other words, the “true talent” run value of a walk is the same as the true talent run value of a non-HR hit. That’s how we would forecast.

See, what happens is that walks are an indicator of talent, much more than non-HR hits. What we care about, in terms of true talent or forecasting (which is essentially the same thing), is what is innate. And walks are more innate to a player than non-HR hits.

FIP is an example of an estimate of what might have happened.

But if you want to predict what could happen, you would NOT use FIP, not in its current state. What you want is FUTURE FIP.

How do we change the classic FIP:

ERA = (13*HR + 3*BB - 2*SO)/IP + 3.2

Here then is my first stab at…

FutureFIP = (6*HR + 2*BB - 2.5*SO)/IP + 5.12

As you can see, in terms of estimating runs, the HR has twice the impact compared to predicting runs. And whereas estimating the impact to runs is more on the walk than on the K, it’s the K that is more predictive than the walk in terms of runs.

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Batted ball exit speed: there’s no question that an exit speed of 95mph is much more preferable than 90mph IN THAT INSTANT: a .363 wOBA v .244 wOBA, as of today, a difference of close to 120 wOBA points. That is, to estimate what might have happened, and not knowing anything else, 95mph has much more value than 90mph. But in terms of forecasting, the value of 95 is not that much higher than 90. As we will learn in the coming weeks, it’s about half that much, about 60 points of wOBA. That is because we are trying to infer what does 95mph tell is about a hitter compared to 90mph.

So, we have to be careful what we are trying to do, when using data, if it’s trying to predict the future, which is the same thing as establishing the true talent level, which is the same thing as establishing the expected value.

Or if it’s trying to estimate what happened, which means taking as a point of fact the event that happened, and assuming SOME circumstances as having happened, and other circumstances as not having existed.

These two things have some relationship, but they are really two different answers to two different questions. And both questions are valid in their own way.

Remember this, as we get more into this in the coming weeks.

]]>The data is binned for ease, and I can certainly make it more granular. We’re plotting balls hit based on hang time, and how far Eaton’s starting position was to the ball’s final landing spot.The top chart is saying “given that an out was recorded, what percentage of those outs were made by Eaton”.

As you can see, if a ball was over 140 feet away, he was rarely involved. And over 180 feet away, he was never involved. That’s the data in brown.

The bottom chart is simply the BABIP. By itself, it doesn’t speak to Eaton. But in conjunction with the top box, it will. All that BABIP in the brown box has nothing to do with Eaton. We can discard it.

The purple boxes shows a ball with a long hang time and close to Eaton: Eaton made all the outs, and the BABIP was .000, meaning that every ball was caught. These are the easy outs. We can discard these too.

The green boxes are balls that are in the air a long time, and multiple fielders can hog the play for the easy out. We can discard all of these as well.

The orange boxes are balls where they were essentially 50/50 plays, and only Eaton was in a position to make the play. These plays we are HIGHLY interested in.

The little yellow box is the one where we worry about “zone sharing”, balls where multiple fielders COULD make a play, but it’s a 50/50 play. Basically, a ball hit right at the boundary between two fielders. Not shown here is the frequency of such plays: 1%.

So, there you have it, we’ll be in a position to figure out how many balls are “easy outs”, “easy hits”, “shareable tough plays”, and “individual tough plays”.

Winter is coming…

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Alan has a terrific article on the subject, with this descriptive chart, where he shows you need a one inch offset to maximize distance, with a cost of a few mph of exit speed.

A word first on optimal launch angles. For a hitter, he maximizes hits at 12 degrees, but he maximizes home runs at 28 degrees. At 20 degrees, it’s a “worst of the best of both worlds”: you still get great results at 20 degrees, just not as good as at 12, nor as at 28. Indeed, one can even reason that a batter’s ideal launch angle is 20 degrees AS A MEDIAN, knowing that if he “mis-angles” a bit low or a bit high, he’ll get better results.

So, what I like to do is create launch angle bands in groups of 8 degrees. We have the two ideal launch angles of 8-16 (median of 12) and 24-32 (median of 28). Then we fill in the rest: under 0, 0-8, 16-24, 32-40, 40+.

Repeating the methodology of the study I did last night, I now focus on the vertical launch angle, to see if there’s any difference. And we do see a difference! Remember, I am looking at the same hitter-pitcher pairs year to year. And the hitters are getting fewer groundballs, a bit fewer balls in the traditional liner angle (8-16 degrees), and a bit more balls where we’d normally see HR (24-32 degrees).

Now, this could happen either because they are “mis-angling low”, meaning they are keeping the same attack angle, and just getting under the ball more. Of course if you do that, your exit speed will ALSO decrease. Which we don’t find. Or, the hitters are changing the way they are batting, just slightly enough, that they are launching the balls higher with a matching attack angle. And maybe improving their approach slightly so they get a bit more of the ball (1 mph of higher exit speed), and at an angle that leads to more HR.

UPDATE:

Doing the same “shifting” exercise, the numbers are fairly close if we compare counts this year to counts last year when off by 1 degree:

]]>Also shown in the figure and table are the results of shifting the 2015 data by +1.5 mph, producing a curve that overlaps essentially perfectly with the 2016 curve and results in the approximately the same number of home runs.

Alan looked at all batted ball events without controlling for the identity of the batter or pitcher. It’s simply the sheer quantity of batted ball events. If the talent changes year to year, which it obviously does, then this will have some effect. How much? Well, that’s why we study these things.

Let me try something a bit different. What I will do is look at each batter-pitcher combination through June 30, 2015 and compare that to their performance in 2016 through June 30. For example Greinke faced CarGo 7 times in 2015 that resulted in a ball in play and 7 times in 2016 that resulted in a ball in play.

In 2015, two of those went for over 100 mph, two went for 90-100, and 3 went for under 80. (Note to kids: don’t average these numbers. You’ll hurt yourself.)

In 2016, two of those went for over 100 mph, two went for 90-100, and 3 went for under 80. Exactly the same thing.

I repeated this for all pairs of players. There was 4860 pairs. In the cases where the number of PA was higher in one year or the other, I prorated down to the lower number of PA. This way we have the same 4860 pairs of players in both groups, and each of them is of the same size year to year. (I didn’t control for park, but that can always be added in.)

We end up with 7656 PA. In 2015, 1605 of them resulted in a batted ball over 100mph. But in 2016, it was 1858, or 16% more. While not as large a number as Alan’s, it is still quite significant. On the flip side, in 2015, 1872 resulted in a batted ball under 80mph, while in 2016 it was 1746.

Now, let’s do like Alan did and “shift” the numbers. Rather than comparing number of batted balls over 100mph in each year, let’s count those over 100mph in 2016 and those over 99mph in 2015. In effect, we’re saying that batted balls are being hit 1mph faster in 2016 than 2015. What do we get?

An almost perfect match! So, it seems that balls are being hit harder. And we know that we’re getting more HR in 2016 than 2015. All evidence shows balls are being hit harder. But we don’t know the reason. While the ball is the easy culprit to point to, it could also be other things, like the strike zone, or the approach of hitters, or a combination of factors. For example, if hitters are deciding to be a bit more patient, they’ll pay for it with more strikeouts, they’ll get more walks, and they’re waiting for better pitches to hit, which they can then hit harder. And, we do have more K, and we do have more BB and we do have more harder hit balls and we do have more HR. We don’t have to rush for a reason. We can just let the data speak as best it can.

]]>What he did was straightforward: starting with game 1 of a series, made sure that game 2 was played on the next day. He used the end-of-season win% of each team to Log5 their win expectation, and then adjusted for home/away. So, a .550 v .450 team would win 64% of the time at home and 56% of the time on the road.

He compared the actual win% with the expected win%, based on how many outs the previous day’s starting pitcher recorded. For example, when the SP of the previous day recorded 12 outs, meaning he was knocked out after 4, or before he could get the first out in the 5th inning, the current day’s team won 49.3% of the time. The expectation, based on the quality of the team and home/away was 47.7% wins. That is, they won MORE with the depleted bullpen. And this example is the LARGEST gap James could find, at only 1.6 standard deviations from the mean. That’s the z-score. When you slice up your data into 27 bins, you are going to find some outlier. Having the most extreme at only 1.6 shows you’ve found nothing. The standard deviation of these 27 z-scores was 0.90. If it was purely random, it would be 1.00. If there was something to find, the SD of the Z-scores would be greater than 1 (and the greater the effect, the larger the number). Instead, it was less than 1, almost certainly pointing to a bias.

Now, James admitted that the next phase of the study is to control for the identity of the SP, which is one source of the bias. Since a great pitcher rarely gets knocked out early, the next day’s SP is more likely to be a good pitcher rather than bad pitcher. So, rather than the expectation of winning being only 47.7%, it will rise to…. well, how much could it rise to… 48.1% or 48.8% or something? Whatever it is, it won’t be say 51.2%.

So, yeah, people say all kinds of things that sound reasonable. But, it’s on THEM to prove it’s true, and not on us to prove they are wrong. Therefore, unless someone provides evidence to support their claim, the claim is a summary opinion without evidence. And we know what I call that.

]]>Adam Eaton has become the go-to guy for Daren and I to look at fielding.

On the left, you will see where MLB RF position themselves. That top image is the angle relative to home plate. Zero is up the middle (home to 2B bag to the wall). +45 degrees is the RF foul line. We can see therefore that RF position themselves at around 25-28 degrees. The bottom image is distance to home plate, and we can see MLB RF are around 285 to 300 feet from home plate. Not shown here is the split between v LHH and v RHH. I’ll talk about that in the future, as there’s something interesting there, in addition to the CF.

Adam Eaton matches the mode of the league, at 26-27 degrees, and rarely ventures outside that zone. How does that compare to other outfielders? I don’t know yet! But doing this for all RF is one of the next items on the agenda, not to mention seeing differences by park. Distance to home plate shows that Eaton definitely plays a bit shallower, at 275-290 feet, about 10 feet shallower than the league RF. Given the results that Daren tweeted the other day (see image below), those ten feet might be what he needs. Still too early for conclusions, but we’ll get there.

]]>The through-counts is what you want 99% of the time. The at-counts is what is USED 99% of the time. Unfortunately, the at-counts is what is used because it is alot easier to calculate: you don’t need to figure the final outcome of the PA. But, what we actually care about IS the final outcome of the PA.

The chart is sorted, generally, by wOBA, but you can see it based on the ball-strike count. The last column, “diff”, is the average difference between each of the component rates, and what the rates are at 0-0 count (i.e., MLB average). So, we can see that if ever you proposed a 3-ball, 3-strike rule, you can look at the “through 1-0” count, and see the results. Scoring will go up by about 25% (scoring moves based on the squared increase of wOBA), but that will come almost entirely to the increase in walks.

The count that shows the least difference is the 1-1 count, meaning that we’d have a 3-ball, 2-strike rule (instead of 4/3). Scoring would drop by about 10%, but that would be almost entirely due to the (further!) increase in strikeouts. You are basically in a pick-your-poison: more walks or more strikeouts? Because changing the ball-strike rule won’t allow you to decrease both. And it will definitely allow you to increase BOTH. If you look at HR, XBH, singles, and you will see that it doesn’t change much. The “wOBA on Contact” is .372, and it goes as high as .406 (3-1 count) and as low as .357 (0-2 count).

]]>Interlude: Just to give you some context, in MLB, Marcel adds 240 PA of league average stats, meaning that at 240 PA, half of what you see is real, and half is random variation. (If you did it by component, it would require much less regression for K/PA and much more for BABIP.)

Jeff is showing you need 1000 PA at Division 1 play to regress half toward league average. For JUCO, it’s only 400 PA.

]]>Indeed, way back when I introduced the Pitch Count Estimator, I used a Markov Plate Count to develop my model. I then used that concept to came up with a quick model as noted in the above link. So, I’m glad that Jim is able to shine his light on this very underdiscussed concept.

]]>Note that between the 2000 and 2001 season, MLB changed the defacto strike zone (in enforcement if not on paper). The result was a change in runs scored, but most noticeably a drop in the walk rate by 14%, the largest single-year change since 1888, when the ball/strike rules were still in flux. Coupled with that historic drop in walk rate was an historic jump in HBP rate, 22%, the highest since 1893. When you talk about “unintended consequences”, this is probably a good example.

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There is not a bright line between a proper, normal projection and the “hot (or cold) hand effect.” For example, in the Zweibel study, they compare performance to “expected” after a hot and cold streak, where “expected” doesn’t include weighting more recent performance and they call the difference the “hot hand effect.” That’s why their numbers are so large. However, if “expected” is a projection which includes a “normal” weighting (say .999^days ago) all of a sudden the “hot hand effect” is a lot less. And what if we made the weighting even more aggressive than that (like .998 or .997^days ago)? The “hot hand” effect might disappear. Or what if we made the weighting curve not real smooth so that, for example, we put substantially more weight on the last week and the hot hand effect completely disappeared? Would that mean that there was no hot hand effect at all?

How do we define a hot hand as opposed to a normal projection which always weights more recent performance? One way to define it is that it has to be transient, right? It has to slowly dissolve and then disappear completely. But, then again, even in a normal projection, we assume that talent constantly changes anyway, even over and above normal aging. That’s why we have a “recency weighting” in the first place. If true talent never changed, there would be NO reason to weight performance by recency. Performance 10 or 3 years ago would carry the same weight as that from one month ago (independent of the number of opportunities - sample size - of course). Even if we still assumed that talent changes with age, if we adjusted for age using some kind of aging curve, we still would not need to weight for recency.

So there is indeed a blur between using a hot hand effect in a projection model and a normal projection model.

So I cannot really answer the question, “Do you think we can identify a larger hot hand effect with better inputs?” because I am rejecting the notion of the “hot hand effect” in the first place for the above reasons. Do I think we can improve our projection models, especially with respect to changes in talent, whether they be transient or somewhat permanent (there is no such thing as a permanent change of talent)? Sure. We are constantly trying to do that. Separate signal from noise. Will the batted ball data from Statcast help with that? Absolutely. Do I think we will make great strides in terms of estimating talent at any point in time using this or any other data or inputs that may come along? No I don’t. I think there is a practical limit and I think we have been close to that limit for a long time. In fact, Marcel probably gets you 90% of the way there and everything else now and in the future probably gets you from 90 to 100% in terms of that limit and we are probably 96 % or 97% of the way to that limit with only 3 or 4% left.

]]>So, when it comes to fines, it says that the fine is 1/110th of your salary. But, clearly that’s based on your ANNUALIZED salary, and not on your one-day salary. Which means the fine would have to be for over 13 thousand dollars, more than he was actually paid! I suppose there’s a cap on the fine.

Therefore, I’d like to see clarification on that, both that the fine is based on annualized salary, and secondly, if there’s any cap in place.

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