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Baserunning
Friday, March 12, 2010
I’ve always had it on my to-do list, and, well, Max did it. But, I’m skeptical of the results as it pertains to Brock:
Baserunners: Willie Wilson, Vince Coleman and Lou Brock were the best once you factor in the battery against which they were stealing (Wanna know about Rickey? Fifth, behind Tim Raines; best among the active players? Carl Crawford, barely missing the top 10); finally the worst three on the basepath, Retrosheet era, are Duane Kuiper, Minnie Minoso and Greg Gross.
As I wrote in the comments:
Great stuff Max. I’d be interested to hear more about Brock v Raines. The gap between the two is 130 SB and 161 CS. It’s hard to believe that Brock can be in the same ballpark as Raines. What you are proposing is that Brock ran against much tougher pitcher/catchers.
Friday, February 26, 2010
Rod Carew.
Wednesday, February 17, 2010
Pizza Cutter has an article at ESPN and BPro, and, I wonder if he made a faux pas:
Without going into all the algebra, it says Perlozzo needs to be 73.2 percent sure that Rollins will make it before he sends him. So, if third-base coaches leaguewide are playing the game correctly, we should see that about 73 percent of the runners in this situation wind up scoring.
No, that is incorrect if “this situation” is man on 3B and less than 2 outs. The MINIMUM success rate has to be 73.2%, meaning that if the third base coach is 73.2% to 100% sure of making it, then you send him. So, the average will be more than 73.2%. How much more? Well, there are tons of gimmes here. Let’s say that one-fourth of the time you have a gimme (say 96-100% success rate), one-fourth of the time you have a great chance of scoring (say 83-87% success rate), one-fourth of the time are the borderline plays (say 70-76.4% success), and one-fourth you have a less than 50/50 chance (say 0-50% success rate).
So, if this is true, then this is what happens:
98% success x 25% frequency x 100% attempt
85% success x 25% frequency x 100% attempt
73.2% success x 25% frequency x 50% attempt
25% success x 25% frequency x 0% attempt
The average is 87.8% for this illustration. For that third case, you can argue that you should run 0% or 100% of the time, and it won’t matter because it’s so close that it’s borderline. And therefore, the success rate if you never run on the borderline plays will now become 91.5%, and if you always run on the borderline plays, it’s 85.4%. That is, under this illustration, your run expectancy is maximized when your overall success rate is anywhere from 85.4% to 91.5%.
On the other hand, if he means “this situation” that includes the distance of flyball and speed of runner, such that these parameters would lead to a 73.2% success rate for these subset of plays (my third line in the 4-line illustration), then okay. That’s not how I read the line the first time, which is all well, because it gave me a chance to make the point. Re-reading it, I guess Pizza could have meant it the right way.
The rest of the article doesn’t go back to Pizza’s quoted line above, and is an otherwise good piece of trying to show that the thirdbase coach is too conservative. The knockout is this line:
Here’s an interesting one: What would happen if third-base coaches just sent everyone, playground-style, on these potential sac flies, regardless of whether it was a good idea? It turns out that teams probably would score more runs than they do now.
Indeed, it was rare that it was a bad idea to send the runner, even after controlling for the distance of the fly ball and the speed of the runner. It was almost always the case that the chances of the runner succeeding were above the break-even point.
The article audience doesn’t lend itself to Pizza presenting his evidence here. Maybe he’ll show it to us, though I have a vague recollection that we talked about this when he was at statspeak.
Wednesday, February 03, 2010
In his career, he has 83 SB and 11 CS. Last year, he was 23-0, as Tommy points out. I remember many many many years ago, when I was a teenager, Raines was still in Montreal, and I just got my first computer. One of the first things I did was try to calculate the breakeven point for steals, by quality of stealer. For example, let’s say that Tim Raines tried to steal only one base all season. Obviously, he would take the most favorable one, the one where the pros outweighed the cons the most. I would give him .999 SB and .001 CS. Now, what if he stole in the two most obvious steal situations. Maybe in this second one, he would be successful .995 times and CS .005 times. See where I’m going here?
So, I fired up my Lotus 1-2-3 spreadsheet, and put in some numbers and tried stuff out. I tried to get to 70 SB and 9 CS, which is a Raines staple. In order to get there, I started him at .999, and dropped by .003 for each steal attempt (0.00289548847776695 if you want to be Excel-exact). By the time I got to the 79th steal attempt, I was at .773 SB. The sum of these 79 was 70 SB. So, I reasoned, Raines was still leaving steals on the table, based on this analysis. What if I kept going? On the 80th steal attempt, he’d be at .770 SB. By the time I get to his 104th attempt, he’s at .701. Basically, we might say that he reaches his personal breakeven point at the 104th attempt. And, adding up all of these steals gives us 88 SB and 16 CS. In some sense, I’m saying that a guy who steals 70 and gets caught 9 times would steal 88 and get caught 16 times.
Interestingly, Raines once had a 90-14 season. Sweet, right?
And, what if Raines decided that his body couldn’t take it, and instead, attempted only 55 steals? In that case, I simply keep the same pattern, but stop him on the 55th attempt. In that case, the sum of his 50 best steal situations is 51 SB and 4 CS (actually 50.6, 4.4). And, interestingly, Raines once had a 50-5 season. I know, I know. I love this stuff, especially if I can apply it to Raines.
Back to Utley. He averages about 15 steals and gets caught 2 times. In order to get that, I can start him as a .999 base stealer, like Raines, but drop him by .0146 after each attempt (about 5 times the degradation rate of Raines), so that he ends up at 15 SB, 2 CS. His last attempt has a success rate of .766. This is what his chart looks like:
Attempt Success
1st 99.9%
2nd 98.4%
3rd 97.0%
4th 95.5%
5th 94.1%
6th 92.6%
7th 91.1%
8th 89.7%
9th 88.2%
10th 86.8%
11th 85.3%
12th 83.8%
13th 82.4%
14th 80.9%
15th 79.5%
16th 78.0%
17th 76.5%
TOTAL 15.00 SB
If I extend it until his last attempt is just over 70%, I get 18 SB, 3 CS.
18th 75.1%
19th 73.6%
20th 72.2%
21st 70.7%
That basically becomes the maximum attempts he should make. UNDER THIS ASSUMPTION. There’s no reason I needed to start him at .999. I could have started him at .9055, and dropped him at .003 like Raines. In that case, his 17th attempt is at 85.9%. In order to get to 70% as the marginal rate, I’d have to extend him to 72 attempts (and he’d end up with 58 SB and 14 CS). As you can see, it depends.
I have to believe that runners are mostly running at their optimal frequency levels, if not pretty close to it. Anyway, I spent (or wasted) lots of hours trying different runners like this. It was loads of fun.
Tuesday, January 12, 2010
Crashburn gives it to us:
It’s highly unlikely that the Phillies lucked their way into teams as consistently elite as their defensive squads have been.
...
From 2002-07, the Phillies were either first or second in the NL in drawing walks.
...
I’ll conclude this with perhaps the most damning bit of evidence that the Phillies are Sabermetrically-inclined: base running.... the Phillies have been not only elite but once again consistently elite. It’s one thing to have a fluke season here and there but the Phillies are incredibly consistent.
...
The Phillies’ success rate on the base paths will astound you:
* 2004: 79%
* 2005: 81%
* 2006: 79%
* 2007: 88%
* 2008: 84%
* 2009: 81%
Once again, not just elite, but consistently elite.
Whoah. Really? From 2004-2009, the Phillies have stolen 701 bases and been caught 151 times. Tim Raines for example is 808/146. Joe Morgan is 689/162. Kenny Lofton is 622/160. Willie Wilson is 668/134. These 4 guys averaged 697 bases and caught 151 times, numbers virtually identical to the Phillies. Imagine that. The Phillies as a team, as efficient base stealers are somewhere between Raines, Morgan, Lofton, and Wilson.
Tuesday, October 20, 2009
By , 09:55 PM
I don’t have to tell you about all the gaffes in tonight’s Yankee game. If this game isn’t the tipping point for more instant replay, nothing will be. Let’s recount the mistakes by the base runners and the umpires, and we are not even through the 5th inning:
1) Swisher gets picked off of second.
2) Umpire misses the call. Swisher appeared to be out by about a foot.
3) Umpire rules that Swisher left early on the tag. Unless he has the greatest peripheral vision in the world, he is not even looking at Swisher when the catch is made and the replay appears to show that Swisher did not leave the bag early.
4) Posada advances one base from second on a double. Absolutely horrible judgment on his part.
5) Posada and Cano completely botch their base running on the come backer by Cabrera. I have no idea why Cano is standing next to the base and not on it. In that situation, both runners usually just stand on the base and one of them is tagged out. In this instance, for no apparent reason, both runners are standing 2 feet off the base and they are both tagged out.
6) For some bizzarre reason, the same umpire that blew the Swisher tag-up call (the esteemed and highly respected Tim McClelland) rules that Cano is safe. I have no idea why. If he didn’t see both runners off the bag, he is blind.
A truly bizarre, extremely badly played and umpired game so far.
Monday, October 19, 2009
By , 07:01 PM
You can’t steal on Pettitte. You can’t “guess” because he throws over too much. You can’t time him or read his move because you can’t tell if he is going home or to first until it is too late. You can’t steal on him period. Is that so hard to comprehend?
Monday, October 12, 2009
Where should he be standing, and when should Punto be looking for him?
The third-base coach should be at one of two spots, depending on the situation:
1. close to the third base bag
2. half-way down the line
Until the ball clears the infield, the third-base coach has go to be close to the third base bag. Punto could already figure out that if the ball would have gone through, that it would take 4-5 seconds (at least) before the ball is picked up in the outfield, and he can just bear down and run.
If Punto does NOT see the coach at 3B, he has to believe that the ball has already gone through the infield. What value exactly does the coach have by being half-way down the line, if it’s an infield single?
Punto’s job is to pick up the thirdbase coach AT THIRD BASE. He’s supposed to look for him. He either did not see him, or was not looking for him. It’s hard to tell from the replays and angles I saw. It looks to me like he didn’t even look for the coach, and just kept his head down the whole way (which is always a b.s. way to run other than the first 10-20 feet).
The bottom-line is this: unless the third-base coach tells him to stop, Punto has to run. And, the ONLY sequence of plays that would have the coach make him stop running is if the ball remained in the infield. Had the ball gone through the infield, Punto would be waved in (implicitly or explicitly), simply because it would have been several seconds before the ball reaches the outfield.
Unless someone could show me why I’m wrong, 100% of the blame goes to the coach. If you want to add a minus to Punto for not looking up at the coach, fine. But, if Punto is looking up, he should be seeing the coach near the bag, not halfway down the line. Punto’s expectation, had he looked up, would be to see the coach near the bag.
Monday, September 28, 2009
The academicians are giving us this:
A couple of points which seemed confusing (to me) when I read the blog post:
- Frank Morgan, the blogger, writes in the third person
- the first study mentioned (22.2 seconds) is specifically about “station-to-station” ball, where you do not overrun the base, and follow the baseline in a straight line, starting/stopping at each base
- the circular-type of optimal paths presumes you are going for an inside-the-parker from the outset
- it’s hard to tell what the maximum velocity is attained in each leg (base-to-base), and how much slowing down occurs (if any), as the runner steps (or sets himself to step) on the base
Anyway, I’m looking forward to reading the full study, as my interest is piqued.
When we talked about hustling a few months ago, I guessed:
I will guess a fast player would have an inside the parker in 13 seconds. I reason 3.1-3.2 for each base, plus an extra 0.5 or so “startup”.
Another way to calculate is to imagine he’s running in a circle with diameter=127 feet. That gives us a circumference of 400 feet, or 122m. Olympic runners do 100m in 10 sec, and the other 22m would take 2 sec, for 12 sec. So, it seems reasonable that very fast, but non-Olympic runners, with not ideal track conditions will take it in 13 sec.
I will guess that a slow runner (by MLB standards) would run around the bases in 18 seconds? Pure guess here.
The Guiness record according to the blogger is 13.3 seconds (set 70 years ago, which tells me no one is seriously making a run at this record, for whatever reason). All to say that the sanity-check model (my model) sorta supports the numbers we see.
Glove-slap to Neyer.
Wednesday, September 23, 2009
Silly: you don’t get a SB credit for it.
Correct: you do get a BB credit for pitching indifference (IBB).
It’s the same thing! What they should be doing is counting the defensive indifference as an official category, AND report on it exactly as they do the IBB. Thanks to Retro and B-R.com, we don’t have to rely on “official” categories anymore. We can report it exactly as we need to. And I report it as:
SB, DI, BK, WP, PB, CS-safe, PK-safe, CS-out, PK-out
all mutually exclusive.
That’s the job of the data recorder. The data analyst can pick and choose how he wants to combine all that.
Sunday, September 06, 2009
From contact to the time the ball was thrown back in the infield, it was 10 seconds. Figure another second from relay back to the 2B bag, and you have 11 seconds. Justin Upton, the batter, got to first base. It could have been a legitimate triple (which you can do in around 11 seconds). What a disgrace for such a young player.
Friday, September 04, 2009
Max says: nuthin‘.
Tuesday, September 01, 2009
By , 07:09 PM
Here is the link:
http://actasports.com/sows.php
They found that:
When no throw was made, stolen base percentage was 75%
When at least one throw was made, 64%
Please tell me that they controlled for the pool of base runners and used some kind of “delta method!” The difference seems awfully high after controlling for the runners.
If not, seriously, I am going to give up on them, as much as I like Dewan. They can’t possibly be that ignorant, can they?
Tango, can you ask them?
Thursday, August 06, 2009
By , 02:24 PM
Even if they both have have a normal platoon ratio and the righty pitcher is a better pitcher (after adjusting for the platoons)?
I was watching the MIN/CLE game today and it was a one run game in the 8th inning. MIN brought in Chris Gomez to run at first base with one out. He was clearly looking to steal a base. CLE took out their lefty pitcher, Sipp, and brought in Smith, a RHP, to pitch to Cuddyer, a RHB. Normally this is the correct move of course because of the platoon advantage.
However, what if the difference in the chances of a successful SB were significantly different off the RH and LH pitcher, which it usually is? Could it be that it is correct to leave in the LHP to prevent the SB? Obviously it depends on the expected success rate versus the righty and the lefty and the catcher behind the plate, as well as the chances that the runner will even attempt a SB. In this case, Gomez is extremely fast but he has poor base stealing numbers.
Anyone want to do some analysis on this?
Friday, July 24, 2009
Pirates coach on Dan Fox:
Every week Fox e-mails a baserunning report to Pirates third base coach Tony Beasley. “At first I was skeptical,” says Beasley. “Now I think [Fox is] a genius. The numbers reveal things you don’t really see with your eyes. You see that last year [first baseman] Adam LaRoche didn’t go from first to third a lot and didn’t take a lot of chances in general. Now he’s being more aggressive and is one of our best base runners. These numbers give the players something of substance to work for. Players want to hear the truth as long as you can back it up. And now we have numbers to back up everything.”
Joe Maddon:
If the opposition is consistently trying to stop Carl’s running game, a hitter behind him is going to see a better pitch, and that’s something that’s hard to define or quantify. “Though I have no doubt,” he adds, “that someday, probably soon, someone will.”
The Book, p. 325, Table 131. It is basically a “with or without you” (WOWY) of the best baserunners, and how batters hit when those guys are on base and when they are not.
And the future:
Cameras used for digital tracking systems will not only measure the exact speed and location of the ball on the field but also the movement of players as well; who, for instance, takes the most efficient routes from first to third, or second to home? “That will change the conversation quite a bit,” says Fox, who has been meeting with professors at Carnegie Mellon University about building such tracking systems for the Pirates. (A system is being tested in San Francisco.) “The metrics we have now are going to look like vast approximations of what we will have.”
Thanks to Albert Chen, the writer of this piece. An excellent sabermetric primer for the masses. This is what it looked like in SI.
Glove-slap: BtB.
Thursday, June 18, 2009
Dan converts SB, CS, PK into an equivalent-line, depending on the game state they occurred. Basically, it works out as the following: newSB = actualSB * LI . And then he scales it by the run environment. Basically.
I disagree with his using of rolling averages. The run environment of 1969 has nothing at all to do with the run environment of 1968. (Is that one reason Camps shows up so well?) It should strictly be based on the runs per game. It should be fairly straightforward to come up with a function that determines the win value for SB and CS given the runs per game, similar to what I did for the run value for major hitting events.
Also, the reason for the better run value for the PK compared to the CS is that PK do not always lead to outs (neither do CS for that matter). I think 25% of PK keeps the runner on base. It’s one of those silly accounting rules that confuses matters. Ideally, we’d have a PKsafe and PKout, to distinguish between runners who were “picked off” by the pitcher but managed to get to 2B anyway.
Tuesday, May 26, 2009
A reader wrote:
Have you guys ever done a study (or know of someone who has done a study) on the optimal stolen base percentage for a given player? I don’t know the exact number, but let’s say the break-even stolen base percentage is 67%. If a player steals at a rate of 60% with 20 attempts, then the right thing for him to do is to reduce the number of attempts, specifically the tougher attempts against better throwing catchers or tricky pitchers with good moves to first. That’s a pretty easy one. But I also think if a player steals at a rate of 84% (I’m thinking of Tim Raines), he probably didn’t attempt enough steals. Surely ther were situations where he would have had a 73% chance of success, but he didn’t make an attempt for whatever reason (fear of failure? fear of lowering his SB%? saving his legs/body against the brutalness of sliding head-first into second?) whether reasonable or not, he didn’t.
So, a player specific question I’m interested in is: did Tim Raines attempt enough Stolen Bases in his career? Given his talents and success rate, was he playing suboptimally by not attempting enough steals? I think there are a ton of factors that needs to be considered, including stealing 2nd vs stealing 3rd, game situations, possibly helping the batter hit better by staying at first, protecting his body in a long season/career, etc. etc.
The best way to get me to quote a reader is by saying “Tim Raines”. Yes, I have thought about that. Not so much Tim Raines, since he attempted quite alot of SB, but more about Carlos Beltran, who has an even higher SB success rate than Raines, but attempts far fewer bases.
I would guess that the “beating up the body” is the best reason to err on the side of caution. That perhaps a player, be it Raines, Rickey, Coleman, Beltran, Ichiro, etc, could attempt more steals on situations where they think they would be successful 75% of the time, but they don’t do it, because the extra cost on their bodies. If you make the SB worth +.02 wins and the CS as -.04 wins, then a 75% success rate means adding .005 wins per attempt. If there are 20 such attempts that these runners are giving up, they are giving up 0.1 wins in a season (i.e., 1 run). I think it’s worth giving up that run, if it means not having to have their bodies pound against the dirt an extra 20 times on a play that is a bit over break-even.
Friday, May 22, 2009
If you think you can be successful at least once in three (and there are two outs), steal home. As Dan quotes Pete Palmer:
the two-out steal of home is the unknown great percentage play
It’s pretty straight-forward to figure out if you have the RE matrix. The run value of the runner on 3B is .387 (with 2 outs). If he’s out, it drops to 0 (end of inning). If he scores, it goes up to 1.117 (1 for the run, and .117 for still leaving the batter at the plate). So, you can gain .730 runs if you score, and lose .387 runs if you are out. That makes the break-even point as 34.6%. The inning, score also play a role in this of course. The “one in three” rule is pretty good to remember.
Monday, May 11, 2009
Tim Marchman gives us the list of the guys who attempted the most steals, along with their success rates, with a little quote from me:
...a team adds .02 wins to its season total when it steals a base, and loses .04 when someone is caught stealing. ... A team stealing 200 bases in 250 tries would add just two wins in an entire season.
(Hat tip: Ken)
Wednesday, March 25, 2009
By , 05:00 AM
I have been talking about this off and on for a long time.
For every game, one of the coaches should have a list of each pitcher on the opposing team’s time home as well as their catchers’ time to second. Combined is the “time from pitcher to second.” Most coaches do, although I am not sure why they are constantly timing the pitchers during a game. Maybe they don’t have a “list” already prepared (they should) or maybe they are double checking the numbers in the list.
Anyway, as in the article, the combined time should tell a manager each player’s expected success rate against that battery. (One of the team’s saberists or statistician needs to figure out how each time corresponds to an expected success rate under normal conditions given each player’s baserunning ability and speed. That shouldn’t be too difficult.)
Once armed with that info, the manager or one of his coaches should have a list (or he can memorize the “rules of thumb") of when each player should steal or not, depending on a number of factors, such as the score, inning, out, and batter at the plate.
Of course there are other factors that go into the equation which are not so easy to prepare in advance for: The count, the type of pitcher that is likely to be thrown, the weather, ballpark, etc. Baser runners and coaches also need to be aware of pitchers who sometimes use a slide step as well. Most of these things a good base stealer takes into consideration anyway.
At the least, the manager or a coach before the game should go over with all the players, especially the base runners: “OK, you, you and can run today, but you, you, and you cannot - based on the catcher and pitcher times.”
70% (on straight steals) is a bad success rate for a player and a terrible one for a team, even if that is above the break even point. As we have pointed out many times, if you are only running when you have AT LEAST above 70% success rate or so (or whatever is break even), your overall success rate should be higher than that. And that is only for an individual runner. ALL individual base stealers should be at least several points above the break even point. Your better base stealers will be a lot higher even if they are running a lot, since against many pitcher/catcher combos they are 90% or better.
So for a team, you should have your worst runners occasionally running with a success rate of 72% or so. Your next best runners will run more with a success rate of 75%. Your best runners will steal a lot with an overall success rate of 85% or more. So for a team, their overall success rate should be close to 80%! Anything less than that has to be suboptimal.
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