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Leverage_Index
Monday, May 14, 2012
This is a great use of the Shutdown and Meltdown metric.
There’s two ways to approach using metrics: do you want to use the metric to illuminate something that is (possibly) transient, or do you want to use the metric to illuminate something something that is (possibly) persistent?
If the Marlins fans have this feeling that the bullpen has not been doing its job, or the writer of the story wants to explain to the fans that the bullpen has not been a good story to-date, then using Shutdowns and Meltdowns is a great way to go. The whole discussion is based on the past, something that is probably transient, and so, we’re not really looking for answers. We’re just looking to crystallize a story that has unfolded.
If we really care about the short-term future, where players will simply play to their own talent levels (plus whatever random variation that comes with it), we need metrics that better describe that persistent set of traits. Shutdowns and Meltdowns is not necessarily the best tool to use there.
Sometimes you need a Phillips, sometimes you need a flathead, and sometimes you need a hammer. Know what tool to use for the job you need.
Thursday, May 10, 2012
It looks like CJ referenced my 2010 article published in the 2011 THT Annual, and re-printed on the THT website over the weekend. And he doesn’t like the conclusion, not one bit! I’ve sent him an email, seeing if he’s interested in having a discussion with me, so I’ll keep you posted on that.
The article showed that no-Mo would cost the Yanks themselves less than two wins using empirical data, a more robust approach would suggest 2.5 to 3 wins. CJ simply said that he’s positive it’s not less than two wins (and presumably, he wouldn’t be happy with my official stance of 2.5-3 wins either).
I should note that I consider Mariano Rivera not only the best reliever ever (and I have no doubt that’s the same answer as the guy standing next to me would give, even if that guy is named Trevor Hoffman), but also that other than Pedro and RJ, the best possible reliever ever for a pitcher.
So, it’s not a question of how good Mo is. He’s #1, and with a bullet. The question is how much value can such a player have in the role that’s available to him. And my answer is 2.5-3 wins a year.
And that also happens to be the official position of the Yankees and Rivera himself, since he’s being paid at 15 million dollars a year, and wins are going for 5 to 6 million dollars each. And 15/5 and 15/6 gives us 2.5-3 wins a year.
Saturday, May 05, 2012
THT reprinted the article I did from two years ago.
Friday, April 27, 2012
By , 02:38 AM
After reading this article on SBN:
http://mlb.sbnation.com/2012/4/26/2978326/jordan-walden-heath-bell-sean-marshall
I wrote this in the comments section:
A replacement level short reliever allows around .5 runs per game more than an average pitcher.
An elite closer allows around 1 run better than an average pitcher, or 1.5 runs per game less than a replacement reliever.
A typical elite closer pitches around 70 innings with an average Leverage Index (LI) of 2.0. That means that he pitches 140 “effective” innings.
For 140 innings at 1.5 per 9 innings less than a replacement reliever, that is 23.33 runs better than a replacement reliever, or 2.33 WAR.
In other words, an elite closer is worth around 2.33 WAR per season.
What is that worth in the FA market? Almost 12 million dollars. So, Brian Wilson, Heath Bell, et al. are actually underpaid.
Does that mean that a team cannot acquire a very good reliever who can function as a closer for less than market value, based on his expected (projected) WAR? No, it does not. In fact, it is much easier for a savvy team to acquire and/or use a cheap but good reliever as a closer than it is for a team to do so with any other position.
Still, closers are NOT overpaid as a class, especially the elite ones. In fact, you can make the argument that they are underpaid. Some are of course. But, so are players at any other position.
Now, I have not researched the salaries of the typical or average closer, as well as their average or typical WAR, so I could be wrong. But, it seems to me that that is a recently popular refrain among semi-sabermetric types - that closers are way over-paid, and I think it is wrong. My numbers above could be wrong too, like the typical LI for a closer, and the runs above average for a replacement short reliever. I was guessing at those.
Wednesday, April 25, 2012
I don’t have data at the office, so, we’ll just have to go with the idea that teams have been winning as many 1-run games the same today as in years past. I don’t know that it’s strictly true, but it’s likely mostly true. And same with 2-run games and 3-run games. Maybe when I get home, I’ll run the numbers. Or better yet, someone else will do the work in the meantime.
In any case, how much of an effect should we see? Ace relievers are more controlled, which leads to better relative performance. If say your typical ace reliever gives up runs at a rate of 75% of league average (while throwing 90 innings) at an LI of 1.7, maybe the better deployed ace reliever gives up runs at a rate of 70% of league average (while throwing 75 innings) at an LI of 1.8. In both cases, they throw 70 innings in the ninth inning.
Anyway, focusing on the 9th inning, they both have 70 innings, both an LI of 1.8, but one gives up runs at a 75% rate and the other at a 70% rate. That difference, 5%, in a league setting of 0.5 runs per inning, is .025 runs in the 9th inning. Which an LI of 1.8 would translate that to 0.005 wins.
So, of course we’re not going to see the results. If you would normally win .930 games with a 2-run lead with the 70s style reliever, you’ll win .935 games with the 00s style reliever. Random variation would overwhelm seeing any impact there.
Furthermore, the run environment will determine the chance of a comeback as well. In a high run environment, the chance of coming back from being 1 or 2 runs down is higher than in a low run environment. Therefore, the high run environment would demand a low-scoring ace reliever. With a low run environment, you wouldn’t need to rely on that low-scoring ace reliever as much.
By the way, all numbers for illustration purposes only. If someone wants to figure out the right numbers, then by all means, go right ahead. Just be wary of selection bias. Please be wary of selection bias. Don’t choose your relievers after-the-fact. Please don’t choose your relievers after-the-fact.
Wednesday, April 18, 2012
Dan looks at Leverage Index, and Jordan Walden (ESPN Insider).
Walden is an interesting case, so it’s important to understand the four different ways to calculate Leverage Index: gmLI (LI when he faces his first batter of the game), inLI (LI when he faced his first batter of each inning), paLI (LI when he faces each batter of the game), exLI (LI after he exits the game).
Walden had a gmLI of 2.11 and inLI of 2.00, but a paLI of 2.51. Basically, he comes in with the place on fire, and he turns it into an even more precarious situation. But, his positive WPA (+0.53) and WPA/LI (+0.06) tells us that he eventually gets out of the jam more often than average. Or, he leaves it for someone else to clean up his mess (exLI of 2.62, pulled 20 times).
Dan had an interesting number, which is the number of times the reliever entered the game with an LI of under 0.7. We talked about this in The Book, and I think I had the number as 20% of the time, an ace reliever enters the game with a low LI. Note that with a 3-run lead, the LI is around 0.9 to 1.0, so, it’s an even less important situation than that. Basically, “tune ups”. Rivera had only eight such games, but you had several ace relievers at 20 and up. That is the cost of saving the ace for a higher-leverage situation that never came. And so, they had to be given SOME game to be used to stay sharp for future games. (These games were basically drills: little to no value in winning a game, but good to stay sharp for the next game.)
Wednesday, April 11, 2012
Very nice chart by Jason:
Runs ratio leverage
1.00 2.43
0.95 2.31
0.90 2.19
0.85 2.06
0.80 1.94
0.75 1.82
0.70 1.70
0.65 1.58
0.60 1.46
0.55 1.34
0.50 1.21
Taking Aroldis Chapman, the way to understand this is that if you think that Chapman’s run-prevention value over replacement will be equal no matter what his role (or, perhaps more importantly, if you think that the Reds think this), then his innings in the bullpen must be at least 2.43 times as important as his innings as a starter in order to provide equal value. If you think he can be twice as good in the bullpen, then those innings need only be 1.21 times as important.
The way I do the conversion, it’s about 80%. And the LI I use is a “chained” value, so that if you expect the top-end LI to be around 2.0, then you only give your ace reliever a value of 1.5 (since somebody else would otherwise take his spot anyway).
As you can see from Jason’s chart, an LI of 1.5 would imply a 0.60 run ratio instead. That means a starter would need to go from a RA9 (runs allowed per 9IP) of 5.00 down to 3.00 in relief.
So, it should be a pretty rare situation that you’d want to make someone a reliever. Mariano Rivera, Trevor Hoffman, Billy Wagner Papelbon maybe.... guys who are so lights out in relief, that you figure they must have an extra benefit from being a reliever that the others don’t have. And that maybe they would give up runs at 60% of the rate they’d give up as starters (rather than the standard 80%).
Anyway, it’s a nice chart, and paints the picture pretty clearly. Good job to Jason.
Tuesday, April 10, 2012
Bill looks at the Phillies this week, and he’s wondering why Papelbon faces so few tough situations.
Friday, April 06, 2012
Jonah’s a fan. It seems his readers may buy into the concept a little too.
Tuesday, April 03, 2012
If there is a more bullsh!t statement coming out of a fans mouth, or an athlete’s mouth, I haven’t heard it. (*)
(*) Mark Messier exempted.
Studes however decides to test it by looking at whether Game 6 heartbreaks (like the Redsox losing to the Mets in Game 6) leads to an inevitable loss in Game 7. And studes does find several of those types of World Series games, using Leverage Index, in yet another fantastic application of LI to find stories (and combine it with analysis to boot). He’s got the 1991 Twins breaking the spirit of the Braves. He’s got the Royals/Denkinger breaking the spirit of the Cards. He’s got the whole list. Of course, he’s also got the 1975 Reds whose spirits weren’t broken by Carbo + Fisk. For every “inevitable” game one way, there was another game to cancel out that inevitability.
But, selective memory is selective memory, and people will remember all those inevitable wins, and ignore games that led to the exact opposite result, even though the same conditions existed. The lies we tell ourselves to ascertain our power over time and space (**)
(**) That’s the transcript; if you can watch the video, all the better, but if you can’t, you really have to get Rosanne’s voice in your head to feel this… and Phil’s voice as pleasant as you can muster… one of the best SNL commercials ever).
Friday, March 30, 2012
Leverage Index continues to find the stories that would otherwise be left for dead.
Tuesday, March 13, 2012
Good job by Adam:
Consider a simplified one-game situation where you only care about your chances of winning that one game and not how your decisions affect other games. It’s the bottom of the fifth, bases loaded, nobody out. You are down by two already and want to pull your starting pitcher. Should you consider using your closer?
This is a high-boLI situation (2.3 boLI) but a below-average LI situation (.9 LI). From this situation, two things can happen. ...
...
This is actually reflected in Leverage Index, as long as you concede that you are going to use the closer in the game; on average, you don’t get higher-LI opportunities to use your closer by waiting than you do by using him in a high-boLI situation in the fifth. You do get a higher LI if you wait and only use your closer if you come back, but those games are offset by all the low-LI outcomes where you would normally just not use your closer.
One little aside. LI would not necessarily tell you what the average LI is for the rest of the game (unlike win expectancy, WE, which DOES tell you that). Same for boLI.
So, really, what Adam is looking for is the chance that a larger boLI will exist at some point in the game. If you already HAVE the highest possible boLI, AND you MUST use your ace reliever, then the score is IRRELEVANT.
Good job by Adam in putting this point across so well.
Friday, March 09, 2012
Bill James pointed out something interesting, that if you take the best relievers over a 5-yr time span, Mariano Rivera appears twice on the list of best 10 relievers (with non-overlapping years).
If I use rWAR (Rally’s WAR, as found on baseball-reference), the top 10 relievers have between 16 and 20 WAR. Rivera in his first 5 years of relief has 19.1 WAR. In the 5 years after that, he’s at 17.7 WAR. So, these two Rivera time spans puts him in the top 10.
And in the 5 years after THAT, he just missed the top 10, with 15.9 WAR.
And none of that includes his post-season.
The one constant through all the years, Ray, has been Mariano. Relievers have rolled by like an army of steamrollers. It has been erased like a blackboard, rebuilt and erased again. But Mariano has marked the time. This player, this man: he’s a part of our present, Ray. It reminds of us of all that once was good and it will be again.
Sunday, February 26, 2012
Mail:
The Nationals announced today that Strasburgh will have an innings limit this year of 160 and that no matter where the team is in the pennant race he will be shut down at that point. My question to you is, if you could use up his 160 innings as a currency, perhaps in only one or two blocks, how would you distribute his 160 innings to help maximize the leverage of his starts. Would the Nationals benefit more by having him pitch his 160 innings from the start of the season on, or from the end of middle of May on? Or maybe the first 100 at the beginning of the season then the last 60 for the playoff push (end of season)?
It won’t matter which of those innings he gets. A win is a win… except against division rivals and wild card rivals. So, at the least, he should never sit against the Phillies, Marlins, and whatever wild card contenders are expected. To that end, I guess it helps to pitch him later, since you’ll know who your competition is.
The other thing is that when the LI for a game goes under 0.7, then pull him right away.
Of course, there’s a cost to having him warm up and start games, so when they say “160 innings”, they probably mean “160 innings, plus we’ll count the equivalent of 2-3 innings of warmup per game”, and if they expect say 25 starts, then really, they are counting on about 220 or so “effective” innings.
Tuesday, January 31, 2012
This is a followup to this post.
***
Guy, thank you. I prefer being lazy when I can, so thanks.
Ok, this is how it works, and I’m going to have to recalibrate things a bit so it works out to zero. It’s possible for example that David doesn’t give out WPA to pitchers on baserunning events (SB, CS, etc). Not important at the group level.
My first adjustment is to divide all the LI by 1.04 for 1980 and 1.02 for 2011.
The WPA has to be baselined as well. I have to remove 3.87 wins in 1980 and 19.71 wins. It’ll be weighted by IP x newLI.
Anyway, this is Guy’s data, recalibrated:
Year IP.... RA9 WPA LI Role
1980 11210 3.99 23.52 1.05 Relief
2011 14228 4.02 49.11 1.07 Relief
1980 26651 4.42 -23.52 0.98 Starter
2011 29299 4.44 -49.11 0.97 Starter
1980 37861 4.29 0.00 1.00 Total
2011 43527 4.30 0.00 1.00 Total
Next thing is to figure out how many runs above average each group was. That’s easy to do as the league average minus the particular group, divided by 9, times IP. We now have this:
Year IP.... RA9 WPA LI RAA Role
1980 11210 3.99 23.52 1.05 377 Relief
2011 14228 4.02 49.11 1.07 447 Relief
1980 26651 4.42 -23.52 0.98 -377 Starter
2011 29299 4.44 -49.11 0.97 -447 Starter
1980 37861 4.29 0.00 1.00 0 Total
2011 43527 4.30 0.00 1.00 0 Total
We then need to convert the runs into wins. Since both have the same run environment, we’re going to use the same multiplier. I have a quick estimator that is simply RPG+5. Which in the above case would mean 9.3 runs per win. If I use PythagenPat, I get 9.4.
Anyway, dividing the RAA by 9.3, and we get:
Year IP.... RA9 WPA LI RAA WAA Role
1980 11210 3.99 23.52 1.05 377 40.54 Relief
2011 14228 4.02 49.11 1.07 447 48.06 Relief
1980 26651 4.42 -23.52 0.98 -377 -40.54 Starter
2011 29299 4.44 -49.11 0.97 -447 -48.06 Starter
1980 37861 4.29 0.00 1.00 0 0.00 Total
2011 43527 4.30 0.00 1.00 0 0.00 Total
Finally, we apply the LI, to get leveraged wins. So:
Year IP.... RA9 WPA LI RAA WAA levW
1980 11210 3.99 23.52 1.05 377 40.54 42.57
2011 14228 4.02 49.11 1.07 447 48.06 51.42
1980 26651 4.42 -23.52 0.98 -377 -40.54 -39.73
2011 29299 4.44 -49.11 0.97 -447 -48.06 -46.62
1980 37861 4.29 0.00 1.00 0 0.00 3
2011 43527 4.30 0.00 1.00 0 0.00 5
LevWins is what we’d expect of their WPA, if those pitchers pitched at those exact levels in every situation.
Instead, what do we find? Well, let’s subtract WPA by LevWins:
Year IP.... RA9 WPA LI RAA WAA levW Diff
1980 11210 3.99 23.52 1.05 377 40.54 42.57 -19
2011 14228 4.02 49.11 1.07 447 48.06 51.42 -2
1980 26651 4.42 -23.52 0.98 -377 -40.54 -39.73 16
2011 29299 4.44 -49.11 0.97 -447 -48.06 -46.62 -2
1980 37861 4.29 0.00 1.00 0 0.00 3 -3
2011 43527 4.30 0.00 1.00 0 0.00 5 -4
I’m not going to make the final adjustment to zero it out, because the point is about to be made.
We see that in 2011, the relievers and starters have a WPA exactly matching what we expected. This would point to having no “matching” of talent to situation. Or, if there was matching (like Rivera) that was undone by bad matching.
But, look at 1980. Relievers were terribly used, getting very little win benefit. Basically, not only was there no matching, but there was severe mismatching. This points to really good relievers being used in really low LI situations.
So, back to 2011. For all the obvious Mo and Papelbon situations, we also have plenty of situations that the managers simply undid those leveraged situations.
Therefore, while they could stand to improve their 2011, they were simply abysmal in 1980.
Sunday, January 29, 2012
This article came up in a search, and given all the new readers around here, I wanted to highlight it:
http://www.hardballtimes.com/main/article/crucial-situations
For those interested in part 2, and part 3, as well as the LI chart:
http://insidethebook.com/articles.shtml
Sunday, January 01, 2012
Great find:
Shawn Chacon got 35 saves as the “closer” for the Rockies in 2004. He had an ERA of 7.11. He had zero saves before that season. He got one more save for the rest of his career.
Chacon was a full-time starter for all but two seasons, one being the aforementioned season. He had a -2.3 WPA in 2004, which is really all you need to know about his effectiveness. He was as bad at home as he was away. He was as bad in save situations as non-save situations. He had as many K as BB, which is a recipe for a disaster of a season, and add in a worse-than-average HR rate, and you get the mother of all closer seasons.
And yet, 35 “saves”. In those 35 saves, he had a WPA of +3.1, or +.09 wins per save. In his other 31 games, he was -6.1 wins (or -.20 wins per game).
If we take a bad Mariano Rivera season to compare: in 2007 he had 30 saves, with +3.7 WPA, or +.12 wins per save. In his other 37 games, he was -1.3 WPA (or -.04 wins per game).
There is one good piece of information to go along with those 35 saves: he had a 1-9 record. As you can see, when Chacon would blow a save, he would blow it really big.
There have been six seasons where a reliever has: saved at least 30 games, won at most 2, and lost at least 8, including Chacon. Four of those pitchers had a respectable ERA, but one, Brad Lidge, had perhaps the worst relief season of all-time. 31 saves, 0 wins, 8 losses, and a 7.21 ERA. His WPA that year was -4.6 wins.
Thursday, December 22, 2011
I don’t remember this, but this was from two years ago, and talked about again today.
Basically, when you calculate the value of a reliever, you multiply whatever value you get by the Leverage Index he merits based on his talent. This would be analogous to figuring out a player’s talent level as a fielder, and then multiplying it by the opportunities he sees (and the opps would be tied to his talent, such that a better fielder will find himself at SS, 2B, 3B, CF, and a worse fielder in the corners or 1B).
If you have a crappy fielder in CF, you can’t punish him too harshly because the team happens to put him there. Think Junior near the end of his career.
Saturday, November 26, 2011
Cardinals and Rangers. That’s how I interpret the CLI stat in the 2012 THT Annual. LI is Leverage Index. CLI is Championship Leverage Index, which basically means how much did each of the 30 team’s chance of making the playoffs change day to day.
So, Studes calculated it, and figured that CLI for the Cards at 2.22, to lead the league. In last place was the Astros. Results are in the sidebars of page 83-84. And by the way, I love the sidebars. Just a great feature and tremendous layout. Much better in this small format, 9x7 book (just a bit bigger than The Book).
I think it’s a tremendous story stat, deserving of tracking on an annual basis. I hope SOMEONE does it, because it’s pretty cool.
from Amazon
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