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This is a great way to use WPA to start to tell a story. They all have the same thing in common: the Brewers were tied or behind, and then after the HR, were ahead or tied. And it was in the late innings in most instances. We could have used THAT as the english-definition (ties or puts you in the lead, in the 7th or later innings). WPA basically refines that by quantifying it in a continous scale, rather than an either/or binary scale.
2009 - 2010
518 , 457 IP
48 , 40 Saves
+6.1 , +3.8 WPA
+41 , +32 RE24
125 , 109 Shutdowns
65 , 71 Meltdowns
WPA would be the best measure, and the Twins were down 2.3 wins. That’s pretty much what we would have expected.
Interestingly, their IP were way down, so, it’s as if they didn’t replace his innings at all.
Consider this simply one data point to the discussion.
Damn, that Phil is insightful. After you see it explained like that, you wonder why it took us thirty years to get to this point. Great job on Phil!
Great job by Max in capturing most of the important ones. I’d also include the base-out situation. And nice to see that he confirms my earlier point about offense peaking at somewhere around 5-6pm, so great job on Max in doing that work!
Matt gives us a historical comparison of Palmer’s method to Patriot’s as well as my shorthand.
Note: I think Pete’s is runs per inning of both teams, not runs per game.
For those who want to play around with stuff like this, I had this chart from a decade ago.
I was going to address this in the Kinsler thread, but I decided to start a separate one. I have always thought it ridiculous for a team to concern itself with finding someone for a particular lineup slot, as in, “We really need a leadoff hitter,” or, “We are so happy with this acquisition because we now have a proven leadoff hitter,” or some such nonsense like that.
You get the best player you can and then you construct the best lineup from what you have. It really doesn’t matter whether you have a “true leadoff hitter” or a “true cleanup hitter” or not. It doesn’t matter at all.
I am not even sure what that means anyway, so let’s try and be more specific in terms of what question(s) we want answered.
I’ll pose two specific questions which we can answer, more or less, quantitatively and which relates to this issue.
1) If we have someone who has lots of speed, or doesn’t have lots of speed, how much can we leverage that attribute (or lack thereof) by placing him strategically in the lineup?
For example if we want to acquire a speedy player that is worth exactly 2 WAR, how much more would he be worth if we plan on putting him at the top of the order versus the bottom of the order, and in doing so, we don’t affect anything else?
One way to answer that is to see how many runs per game we gain when we go from a slow runner to a fast runner at the top of the lineup versus how many runs we gain when we go from a slow runner to a fast runner at the bottom of the lineup. This is very similar to how we figure LI in a game. We compare the impact of a good player or good event to a bad event in terms of WE, at various points and situation in the game.
I took a typical Rangers lineup from last year and ran my sim with Elvis Andrus either batting second or batting 8th, as a great base runner (which he is, so I left his base running projection alone) and as a terrible base runner, like a Prince Fielder. I actually use a 1-5 scale in my sim, so I went from a 5 to a 1. This is not including SB/CS - only advancing on hits and outs by following hitters when on base. I cannot remember if it includes advances on potential WP and PB. My sim probably captures 90% of the value of base running.
I ran 500,000 games of the Rangers playing a team with a RHP. It doesn’t really matter who they played or who the opposing pitcher is. It might matter a little that it was the Rangers with a good middle of the order.
Anyway, with Andrus batting second, as a fast runner, they scored 4.167 rpg. As a slow runner, 4.101 rpg, for a difference of 9.9 runs per 150 games. That is around what we expect from the difference between a great and poor base runner in general (an average slot in the order), so I suspect that my sim is undervaluing base running a little, or maybe it will find around the same difference in any slot, in which case, it is probably capturing most of actual base running value in real life.
Nope.
As a #8, the difference between Andrus as a great (5) and terrible (1) runner is only 4.65 rpg. So yes, you can leverage base running with batting slot, but, this is the most extrme situation possible. My guess is that for an actual team making a decision about a player based on where they think he will bat in the order, or what other players that have slotted in the lineup, we are only talking about 1-3 runs per season.
For example, if a team is indeed looking for a lead-off or second place hitter, and player A has the same value/projection as player B, but player A is a speedster and player B is just average (and assuming that their hitting profiles are the same), how much more should they pay for player A. I think Player B is probably going to be worth only 1 to 1.5 runs more than Player B given that you are leveraging him the leadoff or second slot. I think that most teams is going to way overvalue that speedster. IOW, you should pretty much forget about the fact that you are looking for a player to fill a certain lineup slot. Like many things (clutch, batter/pitcher matchups, etc.) Use it as a tie breaker only.
2) Same question as #1, but what if we changed a player’s OBP by 20 points by adding walks only? How much can we leverage that by batting him lead off rather than 8th? We’ll use the same method.
I did the same thing with the sim. This time I used Kinsler in the leadoff slot or the 8th slot and I ran the sim (500,000 games each) for his normal projection at the end of last year and with his OBP jacked up by 20 points by adding around 10 walks per 500 PA.
Kinsler batting leadoff, normal OBP: 4.146
Kinsler leadoff and an OBP 20 points higher: 4.201.
Gain: 8.25 rpg (Tango, how does that compare to what you would expect for an average player on an average team in an average slot?)
Kinsler batting 8th, normal OBP: 4.163 (why you don’t bat him 8th, BTW - you lose 6 runs a year!)
Kinsler leadoff and an OBP 20 points higher: 8.55 rpg.
Wow, interesting. You cannot leverage his OBP by batting him at the top of the order. I am not sure why. When my computer frees up, I’ll run some more game. Maybe I’m getting too much random fluctuation in 500,000 games. My guess is that 1 standard error even in 500,000 games is still like 5 runs per 150 games, so really, a comparison of 8.55 to 8.25 doesn’t really tell us anything.
I’ll try and run some more games with the speed thing too. Even though the results seem reasonable, there is too much sampling error there too for the difference to be reliable to any degree…
If you are going to compare to a baseline, you CANNOT compare to zero. You have to compare to the actual hitter’s true talent level. It’s not the average hitter that bunts, but a below average hitter. So, we EXPECT to see a negative win value relative to the average hitter, not only on bunts, but on non-bunts too.
Glove-slap: Mike.
Brian Burke does a good job for football.
***
He also signs off with some MGLism:
If you gave the Saints coaches the choice between receiving the punt and letting the Falcons roll the dice on 4th and inches, they’d take the punt every time and twice on Sunday. That tells you something, doesn’t it?
If you are looking for a quick equation:
chance of scoring
= 0.086
* (base + 0.5)
* (3 - outs)
This gives you:
Base Outs ChanceScoring
1 0 0.39
1 1 0.26
1 2 0.13
2 0 0.65
2 1 0.43
2 2 0.22
3 0 0.90
3 1 0.60
3 2 0.30
Close enough to reality for something quick that you need.
If someone wants to come up with something more robust, then play around with those arbitrary coefficients, and post your results!
If you want the actual data.
Colin posted a run expectancy chart for the 9th slot, when the pitcher is the batter and when a pinch hitter is the batter. One thing to be careful about is that the underlying contexts won’t be similar. For example, if you have a pinch hitter at bat, this likely means that the opposing pitcher may be a pretty good reliever. So, it’s an interesting chart, but I’d like to see a more apples-to-apples comparison.
You can also see a related chart here, which I think was 1956-2007, but I’m not sure (I posted the file June 2008).
http://tangotiger.net/retrosheet/reports/re_bo.htm
BASE OUT RE_PIT RE_PH DIFF
___ 0 0.50 0.49 0.00
___ 1 0.21 0.25 0.04
___ 2 0.05 0.09 0.04
1__ 0 0.79 0.84 0.05
1__ 1 0.38 0.50 0.12
1__ 2 0.12 0.19 0.07
_2_ 0 1.02 1.13 0.11
_2_ 1 0.56 0.64 0.08
_2_ 2 0.18 0.28 0.09
12_ 0 1.46 1.40 -0.06
12_ 1 0.69 0.87 0.18
12_ 2 0.24 0.43 0.19
__3 0 1.34 1.39 0.06
__3 1 0.75 0.93 0.18
__3 2 0.22 0.35 0.14
1_3 0 1.57 1.74 0.17
1_3 1 0.86 1.10 0.24
1_3 2 0.26 0.44 0.18
_23 0 1.77 1.96 0.19
_23 1 1.10 1.34 0.24
_23 2 0.28 0.57 0.29
123 0 2.04 2.32 0.28
123 1 1.20 1.51 0.32
123 2 0.42 0.79 0.37
Partial mail:
I’d like to comment on a shortcoming that WPA has as a story stat. Or to use your analogy, I maintain that there are cases where the screwdriver screws the wrong screw. I still love the stat though.
WPA does a great job when the story is batter vs. pitcher. As this is the main story line in most games, WPA gets it right most of the time. However, whenever the story relates to fielding, WPA misses the boat. What bothers me is not so much that it doesn’t credit great/terrible fielding, but apportions both the credit and the blame to the wrong players.
My best example is one of my most favorite games, Yankees vs. Mets on June 12th 2009. With the score in favor of the Mets 8-7, bottom of the 9th, men on 1st & 2nd, 2 outs, A-Rod hit a pop up off K-Rod to Luis Castillo. He dropped the ball and the Yankees won. A-Rod was awarded 0.818 WPA points (I think perhaps the most in his career for a single play!) and K Rod “awarded” -0.818. Neither of them deserved either the credit or the blame.
Similarly, in cases where a 1st baseman makes a tremendous play and turns an unassisted double play by doubling off the runner, the pitcher receives all the credit, the batter all the blame, while the fielder receives nothing.
My response:
There’s nothing in WPA that prevents the awarding of the play to the fielders.
I will point you to the very first time I did WPA, where I go out of my way to talk about Moises Alou, and Gonzalez, and really, all the fielders. NEVER do I say it was ONLY Mark Prior.
http://www.hardballtimes.com/main/article/crucial-situations
That Fangraphs or BR.com does not have the fielding data to do the split is not a problem with WPA. It’s a problem with them, or rather, the lack of data that describes the fielders in detail.
I’ll also point out that WPA only described who was INVOLVED. It does NOT try to establish who was RESPONSIBLE. In the above example, ARod and KRod were involved. Now, it would be nice if Fangraphs and BR.com captures the fielders as well. But capturing fielders is really not so clean. The best example there is that the 1B is simply the end point of the putout, and he is basically “just there”. Was he “involved”? You can make the case that he was involved. You can also make the case that he was incidental.
If someone wants to create a process for WPA that also includes the fielders, then go ahead. You may get to the point that it ends up hiding the story you are trying to show.
Studes calls WPA the story stat. A win expectancy chart (and its associated leverage index) gives you a numeric representation of how you feel watching the game. Imagine that, how you feel, quantified.
Well, Poz unearths a gem of a game with WPA:
Well, Art Shamsky had the greatest WPA ever for a single game. His performance is the very peak of what man can do to win a baseball game. He homered to give his team the lead in the eighth, homered to tie it in the 10th, homered to tie it again in the 11th, there is not much more a baseball player can do. And so what happened? Art Shamsky’s Reds lost to the Pirates that day.
His WPA was +1.50. Remember, with every team starting at 0.50, this means the winning team, as a team, will be +0.50, and the losing team will be -0.50. With Shamsky at +1.50 as the losing team, this means that the rest of the team was -2.00!
Poz asks:
WPA—Win Probability Added—is one of the most interesting statistics out there, and to be honest I do not see why it has not become more popular among mainstream baseball fans. Maybe it needs a better presentation, a better name, a public relations person because WPA, it seems to me, speaks so clearly to what so many baseball fans love about baseball: The winning plays.
And he’s right. WPA was first presented back with the Mills brothers 40 years ago. The first time I talked about WPA was the day after the Cubs/Marlins game, easily the game where you can feel that something huge was happening after every play. And when I posted it on my blog, the readers immediately got it. (also see above chart)
Why do some people give it a bad rap? Because they take the stat out of its comfort zone, the story stat, and lump it in with other stats, where they then try to dissect it, expose its limitations, and then decide that because of those limitations, it has no value whatsoever! It’s like looking at OBP, seeing its limitations (BB = HR), and then deciding it’s crap. Imagine that. The person doesn’t know how to use a screwdriver, and then complains that it can’t hammer in a nail. “Why do we need a screwdriver if I have a nail?” Well, how about I have a screw, and I need that Philips?
I believe the best way to sell WPA, WE, and LI is to hammer home those Cubs/Marlins/Game6 types of games. Because those games can be explained, to a certain degree, numerically. And once you can do that, once you have a process that you can categorize emotional games on a numeric scale, it then becomes possible to find games like Art Shamsky’s.
That’s why we quantify things: to find even more emotional games.
How sure do you need to be to go for it? This reader does the work for one scenario.
Using a different Win Expectancy chart, and trying a different scenario, here’s what I got. This one is a pure sac fly, so the batter made the second out, and you have to decide whether to send the runner or not.
If it’s the bottom of the 9th: if down by 1, you have a .15 chance of winning. If you score, you tie it up, and now you have a .55 chance of winning. If you are thrown out, you are down to 0. So, when you’ve got a +.40 potential gain against a -.15 potential loss, you only need to be safe 27% of the time. If it’s the top of the 9th: down by 1, you have a .10 chance of winning. If you tie it up, you are at .38. Thrown out means 0. In this case, it’s a gain of +.28 against a loss of -.10. That’s 26% of the time.
What if it’s a single (and the batter takes second on the throw): in the bottom of the 9th, it’s .18 chance of winning with runners on the corners, .60 if you score and draw the throw, and 0 if you are thrown out. That’s +.42 gain against -.18 loss, or 30% chance of needing to be safe. In the top of the 9th, it’s .15 winning with runners on the corners, .46 if you score and draw the throw, and 0 if you are thrown out. That’s +.31 against -.15, or 33% chance of needing to be safe.
Basically, go for it if you have at least a 1 in 3 chance of scoring. Sit tight otherwise.
This reader is lamenting the work of Darren Oliver, even though:
Darren Oliver, a key piece in last year’s run to the playoffs and still-quality reliever in the present whose political capital is running out. I say that he is still a quality pitcher in the present not to purposefully antagonize anyone, but because it is the truth: he’s allowing around two-thirds of a home run per nine innings, he’s still maintaining a strikeout-to-walk ratio in the vicinity of 4:1 (though the strikeouts have dropped off this year, from 9.5 K/9 down to 7.9 K/9), his BABIP has plummeted from .310 to .254, and his left-on-base percentage is still sitting in the 76-77 percent range. Hell, his ERA has even fallen by nearly half a run relative to last year. Why, then, are we so perturbed by his mere presence in close-and-late situations?
So, it seems the typical fan is not happy with Oliver, even though on the surface, he’s got all the right numbers. Apparently, the reason is that when it comes time to the context of the game:
Last year, Oliver logged 22 “shutdowns” against just nine “meltdowns,” which wasn’t exactly within the realm of the elite, but was still rather acceptable all things considered. The year before that, it was 23 shutdowns against 10 meltdowns. And one more year before that, it was 19 shutdowns against only nine meltdowns.
But this year? This year, it’s just 13 shutdowns against 12 meltdowns, with that latter figure being tied for the third-most meltdowns by any given relief pitcher in the majors this season, and easily beating out the seven meltdowns incurred by Neftali Feliz and Arthur Rhodes. What had always been a ratio of greater than two-to-one is now a ratio right around one-to-one, or, more specifically, 1.08, which ranks 112th out of 135 qualifying relievers this season. That’s troubling, to say the least.
So, we see that WPA is describing what has happened in a numeric sense, and gives some evidence for why fans are feeling the way they apparently are.
And, to conclude:
That said, win probability and its derivatives aren’t predictive in nature, so I don’t think you can simply go out and declare that Oliver will continue pitching poorly in higher-leverage game situations—hell, he could go out and begin turning that back around tomorrow, and it wouldn’t surprise me too much. It’s certainly worth keeping an eye on, though.
An excellent post all-around.
This is the run expectancy matrix for 1969-1992, which had 0.477 runs per inning.
1B 2B 3B 0 outs 1 outs 2 outs
__ __ __ 0.477 0.252 0.094
1B __ __ 0.853 0.504 0.216
__ 2B __ 1.102 0.678 0.325
1B 2B __ 1.476 0.902 0.435
__ __ 3B 1.340 0.943 0.373
1B __ 3B 1.715 1.149 0.484
__ 2B 3B 1.967 1.380 0.594
1B 2B 3B 2.343 1.545 0.752
This is the RE matrix from BPro, for current 2011 (0.479 runs per inning):
1B 2B 3B 0 outs 1 outs 2 outs
__ __ __ 0.479 0.261 0.094
1B __ __ 0.853 0.519 0.215
__ 2B __ 1.057 0.671 0.311
1B 2B __ 1.431 0.929 0.432
__ __ 3B 1.311 0.928 0.344
1B __ 3B 1.686 1.186 0.465
__ 2B 3B 1.889 1.338 0.561
1B 2B 3B 2.264 1.596 0.683
The numbers in the two charts are quite similar, with a few gaps. Since my chart is based on games played from 1969-1992, and BPro’s chart is based on the five weeks of 2011, we can see why my chart would be expected to be more stable.
I’m not surprised that there is such strong agreement, since the run expectancy matrix is run-environment driven. It’s not like they’d both agree that you start at 0.48 runs per inning, but then be wildly off in some other base/out state.
In any case, I’ve been pleading for baseball to return to the baseball of my youth, and that’s what we are getting. Get ready for the return of small ball! Speedsters, split screens of pitchers/runners, tons of useless pickoffs (that part should be addressed if small ball is the reality), jack rabbits in the outfield. And hopefully the end of highlights that are just run-of-the-mill home runs.
Lots of math, so be forewarned. However, please jump in, because this is my kind of fun.
Michael does a super job of analyzing the situation here.
The situation is this: in the top of the last inning, the score is tied, there are no outs, and the runners are on 1B and 2B. The batter has a 1-2 count with the ball in the air. Do you intentionally drop the ball, keeping the batter at the plate at 1-2, and runners at 1B and 2B. Or, do you catch the ball, let the runners go to 2B and 3B, and a new batter at the plate?
This is exactly what win expectancy is designed to answer. Michael starts with the basic win expectancy data, and then looks to try to make adjustments.
The end result is that the fielder made a defensible and gutsy call to not catch the ball.
Matt. I did not know this: (lgRPG^(1-z)) * 2, where z is between 0.27 and 0.29. Can you show me how this is derived, because I was doing it the brute force way. Sh!t, had I know it was this easy, I would never have proposed my method.
Thanks Brian:
And for your mobile needs:
http://live.advancednflstats.com/WPmobileLive.php.
May 26 13:33
Do pitcher’s reach back for velocity when needed?
May 26 13:18
What makes for a successful GM?
May 26 07:27
“Why Kickstarter works”
May 26 03:03
Pete Palmer’s new book: Basic Ball
May 26 01:11
Largest demonstration in Canadian history?
May 25 19:41
What sabermetrics is NOT
May 25 16:59
Howard Stern
May 25 12:51
Chad Curtis
May 25 11:26
Lack of hustle during a game
May 25 10:58
Rooting for laundry
THREADS
May 26, 2012
What makes for a successful GM?
May 25, 2012
Pete Palmer’s new book: Basic Ball
May 25, 2012
“Why Kickstarter works”
May 25, 2012
Chad Curtis
May 25, 2012
Which pitchers are the forecasters betting on a good rest-of-season?
May 25, 2012
What sabermetrics is NOT
May 25, 2012
Sports pic of the year?
May 25, 2012
Do pitcher’s reach back for velocity when needed?
May 24, 2012
Largest demonstration in Canadian history?
May 24, 2012
Rooting for laundry
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