This is easiest seen with trading deadline deals. A replacement level player does/should/may cost the same to a playoff contender as to an also-ran. But a high quality player has a “multiplier” effect, which is what I think Rachel’s beta is. I didn’t follow too well if Rachel intended to say the Beta changed for each player-team, but I think it must. This is why a contender will trade for a “star rental”.

Anyway, the key point in all this is that we’re dealing with asset value, no different than your house. It’s the value of the house minus the mortgage. And if you can think of the house being potentially modular, how much value it can gain or lose by going to a different location. And how we determine that price.

The one thing I will say is that in multi-year deals, you CANNOT assume that a team will maintain a .550 win% throughout. And even if you did, you CANNOT allow the new player for the player-team beta to be different from all the other players on the team. Basically, the new player is leveraging the team’s fortunes, and so, he can “absorb” that beta all to himself. Well, you CAN, but I think that’s a pretty risky thing to allow the model to do.

]]>It’s like saying that Vlad should chase fewer outside pitches, when in reality, he probably chased exactly the number he needed to. Every hitter and pitcher is optimizing his approach based on his skillset, so, my expectation is that we should likely not see trends.

]]>In this poll, it was 75-12, with a 13% fat-finger. Assuming the fat-finger breaks proportionately to 75-12, we can discard the 13%, and assume the breakdown is 86-14.

HOWEVER, without a fat-finger option, this is what would happen: 13% of the 86 would vote the other way. And 13% of the 14 would vote the other way. The final SELECTIONS would therefore break as: 77-23. Even though the INTENDED selections would be 86-14.

I know this is kind of a goofy little poll, but think of the implications in terms of “hanging chads”. Electronic-elections can rest on fat-finger.

My phone is fairly sensitive, so that I don’t even have to touch the screen, I just need to hover close enough. So it’s possible that on your way to a selection, you end up clicking something you didn’t even click.

I’d love to see research in this area.

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There are two ways to calculate these values. One way was described in Table 5 in The Book. You take the “run value” of the starting state of the event. And then you add up all the runs that actually scored following that event, to the end of the inning. The second was described in Table 7 in The Book. You take the “run value” of the starting state and of the ending state, subtract the two and add up the runs in-between. The results will be very close to each other, either way you do it.The above chart was done the first way (the Table 5 method) mostly because given the dataset I have to work with, it was easier to do it that way.

]]>We can quantify the ball-strike count. If we look at all 0-2 counts, we can see how that plate appearance ended. As you’d expect, it’s highly in favor of the pitcher. We can do the same with all 3-0 counts, and ignoring IBB, you realize that it’s highly in favor of the batter. I published this chart several months back:

The key column there is the overall wOBA, which you can think of as analogous to OBP in terms of impact. But, let’s represent wOBA by its base value, which is runs, relative to the average. Linear Weights Runs, if you think of Pete Palmer. (That’s where the w in wOBA comes from… weighted.) So, for the first pitch, the 0-0 count, the run value of 0 runs. For a 3-0 count, the run value is +0.20 runs. That is, if you pick up a plate appearance at a 3-0 count, then by the time the plate appearance will have ended, that plate appearance would have increased the number of runs in that inning by +0.20 runs. For an 0-2 count, the number of runs scored will be -0.11 runs, relative to the average.

In a further nod to Pete, we can represent the Run Expectancy by Ball-Strike count in this manner:

To read the chart, you look for the ball-strike count you are in, and that will tell you how many runs your team will gain, or lose, by the time the plate appearance is complete, relative to being in an 0-0 count.

Now suppose you hit a homerun. We know that gains you +1.40 runs, via Palmer. But, what if we want to distinguish between an 0-2 homer, and a 3-0 homer? A homer is a homer you say. Yes, you are correct. But, perhaps you are interested in the components of that. What if we want to include Working The Count as a metric? What if we realize that a HR on an 0-2 count is more impressive than a HR on a 3-0 count? It looks like this:

0-2 count HR: -0.11 for working the count, +1.51 for hitting a homer IN THAT COUNT

3-0 count HR: +0.20 for working the count, +1.20 for hitting a homer IN THAT COUNT

Does this help us evaluate a hitter? Maybe. Maybe if the hitter who finds himself in alot of 0-2 counts who can improve his count-skills, he’d be able to leverage his HR skill much more than a guy who already knows how to work the count.

In any case, since everything about baseball is about the strike zone, and by extension the count, then let’s create a metric that recognizes that.

Joey Votto, you will not be surprised, is the best hitter at working the count. For our metric, what we will do is simply count the number of times he is in particular ball-strike count ON HIS LAST PITCH. That is, we’re going to break down his skill set into two components: getting to his last count, and then finishing off the plate appearance. This is how often Votto has reached each ball-strike count, excluding IBB:

Since each ball-strike count has its own value, and we have the frequency of Votto entering each of these counts on his last pitch, we simply multiply one chart by the other. Those 12 times he entered a 3-0 count as his last pitch? That’s worth a total of +2.4 runs. We repeat this for all 12 ball-strike counts, add them up, and we get a total of +5.5 runs.

You can also think of it more simply: Votto’s average ball-strike count on his last pitch was: 1.52 balls, 1.15 strikes. That’s basically his “average” count when he faced his last pitch. An average ball is worth around +0.06 runs and an average strike is worth around -0.07 runs. So, you can simply do: 1.52 x 0.06 - 1.15 x 0.07 = 0.011 runs. Since Votto has 400 plate appearances, that gives us 400 x 0.011 = +4.4 runs. That’s the simple way of treating each ball equally and each strike equally. In the more precise way, we get +5.5 runs.

We can also apply it to pitchers. You will get the boring answer that the two pitchers who can Work the Count the best are Max Scherzer and Chris Sale. Among relievers, you get the even more boring answer of Kenley Jansen and Craig Kimbrel. Jansen’s average ball-strike count on the last pitch is 0.92 balls, 1.60 strikes. Given that the maximum possible strike count on the last pitch is 2 strikes, it’s fairly impressive to get there at all, and to do so with only 0.92 balls is doubly impressive. Contrast to Trevor Rosenthal, who also has impressive Working The Count numbers, by getting there with 1.62 strikes, with 1.44 balls. In other words, Rosenthal puts himself into trouble much more than Jansen. But overall, Rosenthal is still one of the best at Working The Count. That’s how impressive Jansen is.

Stay tuned as we work to get this automated and into leaderboards.

]]>The simple way to think about this is that if you have a pitcher whose peripherals suggest a 10-16 pitcher, and you have another pitcher whose peripherals suggest a 16-10 pitcher, those two, combined would be equal to 2 league average pitchers (13-13).

A 10-16 pitcher is 0 WAR, a 16-10 is 6 WAR, and 13-13 is 3 WAR. That is, in terms of value of production, two 3 WAR pitchers is equal to a 6 WAR and a 0 WAR. Or, simply equal to a 6 WAR pitcher. Teams MUST pay the same. There are plenty of arguments that you will hear and I have read that suggest otherwise. But if that’s the case, the baseline would change to FORCE linearity. For example, say you think a team would pay more for 16-10 than for two 13-13. What therefore would be the equivalency? Paying the same for 15-11 as for two 13-13? Fine, let’s say that.

That simply set the WAR level so that 11-15 is 0 WAR. 15-11 is no longer 5 WAR, but 4 WAR. 13-13 is no longer 3 WAR, but 2 WAR. And two plus two equals four.

It will always work out like this.

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That lone outfielder on the far left, who is being given plenty of tough opportunities is Ryan Braun. Among the 101 outfielders in the above chart, Braun is easily the 101st in frequency of tough opportunities. While he is converting at a below average rate, it’s not that much below average (85th out of 101). Other than that extreme point, it’s pretty much a scattershot of points, with little to no relationship between opportunities and performance. Which is pretty much what we want to see in a metric.

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As you can see, in the very low catch probability zones, we do see the top fielders show off. But, where they make things look more effortless, and where the rest of the field still struggles, is around the 40% to 70% catch probability “zones”.

Indeed, we can even come up with a simple enough function. Whatever the odds for the rest of the field is, the top fielders are at 5X to 6X of making the play (in terms of odds). For example, if the rest of the field makes a play about 15% of the time, or 15 outs per 85 hits, then a top fielder is around even-odds of making the play (50%). If the rest of the field is at even odds, then a top fielder will make the play around 5:1 to 6:1 or about 85% of the time.

You can see therefore that in very low probability zones, say 1 out per 99 hit, a top fielder is still quite limited in terms of showing off, making that play around 5% to 6% of the time. And similarly, in a high probability zone, say 95 outs per 5 hits (95% out), a top fielder will make that play 99% of the time.

]]>I was lucky enough to meet Glenn at SABR, albeit much too short a meeting.

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Suppose team wins 10 extra games one year. Where did the wins come from? Pitching? Hitting? Pythag? Clutch? Power? Walks? ...Let’s break down the 10 wins into where they came from, then we’ll go to step 2. Same thing here: where did those HR come from? ...Once we know if it’s launch angle, speed, spray, air resistance, weather, spin ... then we go to step 2, see if maybe it’s the ball. ...

So, we’re just interested in facts. This is a beautiful point by Phil, allowing us to get data and approaches out there first. Let’s just deal with data.

To start with, before we even talk about HR, let’s just talk about batted balls. So, the question is: where are all those batted balls coming from?

]]>As you may know, the Catch Probability that Statcast uses is based at its core on a distance v time chart, as you see on Savant. If you click on that link, you will see a “line” where all the tough plays happens. That was the first iteration of the metric, and it worked out pretty well. Since then, we’ve incorporated an “is back” parameter, so that any ball in which the fielder has to run +/- 30 degrees from straight back is given one degree of difficulty (essentially it turns what otherwise would be 4-star plays into 5-star plays, and 3-star plays into 4-star plays, etc).

We’re also in the middle of incorporating “wall balls”, essentially removing as an opportunity any basehit where the outfield fence is an impediment to making the play (defined as the ball landing within 8 feet short of the fence or beyond).

From this point, we can figure out a player’s “plus-minus”, much as you’d see with UZR. The baseline however is the generic OUTFIELDER, and not a generic position-specific outfielder. If a ball lands half-way between CF and RF, we don’t say that McCutchen had a 30% catch probability if he was a RF and a 40% catch probability if he was a CF. An OBP is an OBP. As a NEXT step, you can decide to compare OBP by 1B and by C if you want. But, at its core, you want a common baseline as much as you can. That said, you can have BOTH. We can get there.

Now, about the difficulty. We can see whether our current model (the one based on distance/time, the is-back and the wall-balls) works, by comparing outfielders who play CF and who play one of the corners. We do this by using the “delta method”, comparing players in both groups, giving the amount of weight equal to the lesser of the two positions they played. We have 154 outfielders, totalling 5326 plays since 2016. These players, as CF were expected to catch 85.5% of the balls in play (using a generic outfielder) and they actually caught 88.5%, or +.030 outs per ball in play. These same players as a corner outfielder were expected to catch 83.6% and actually caught 86.6%, or a difference of… +.030 outs per ball in play.

We’ve determined therefore that the model doesn’t require a “degree of diffuculty” positional adjustment beyond what we’ve already captured. We can therefore compare a CF to a corner outfielder using the same model, and put them on the same baseline.

AFTERWARDS, we can do positional adjustments. But that’s secondary to the primary focus of the model.

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Bill has a seemingly aggressive weighting, giving more weight to the recent performances. Basically, in the last 365 days, the pitcher is going to get some 60-65% of the weight. MGL likely follows a Marcel-like weighting scheme. That means that the most recent 365 days probably gets 1/3 the weight maybe 40% of the weight.

This year, Scherzer is 0.8 or 0.9 WAR ahead of Kershaw. Last year, Kershaw pitched 65% as much as Scherzer, so that, while pitch for pitch, Kershaw was way ahead, when you include playing time, they were around even. The years before that Kershaw was ahead.

The WARcels has a weighting scheme of 60/30/10, which is fairly close to what Bill uses implicitly. From that standpoint, WARcels has Scherzer even or just ahead of Kershaw.

So, that’s what it comes down to, the amount of weight to give this year, and how much weight to give the playing time. Once you do that, then the answer will become more obvious.

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His main issue is that he basically used a chart like this:

And simply added up those values for every batted ball. Prima facie, this is entirely reasonable. You have a high pop up? Let’s count that as close to a 0 wOBA (i.e. out). You have a 28 degree 115mph shot? Let’s count that as close to a 2.000 wOBA (i.e., HR). And I would say 99% of researchers would do exactly that.But, what if I tell you that the ENTIRETY of a player’s batted ball profile can be determined by the frequency of his barrels? That is, rather than assign a value to every batted ball, let’s only assign one value, the same value, to simply those 6% of the balls that falls into the “barrels” category?

You’d think I’m crazy, right? Look how strong that relationship is, looking at barrels to wOBA the following year (not wOBA on batted balls, but overall wOBA including BB and K!)

Well, Andrew just demonstrated that it’s better to discard 100% of the batted balls, than to include all of them. (Voros is smiling.) And I’m saying, let’s at least START by discarding 94% of the batted balls, and focus on the 6% hit at the ideal speed+angle.

Then, you can start adding a bit more. You can add the near-barrels, those well-struck balls that just missing being barrels. You can add the flares and the burners. And so on. Once you do this, then you’ll be in a much better position to forecast the future.

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