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Scouting
Friday, May 04, 2012
By , 05:39 PM
This does not apply to most pitchers.
I saw Rafael Dolis throw an inning today in the Cubs game. The announcers said they are thinking about using him as a closer (their ace). He has never pitched above AA in the minors I don’t think.
I’ve never seen him before and I know nothing about him.
I am pretty sure he is terrible for one reason and one reason only. He has no idea where his fastball is going, presumably because he cannot repeat his (very simple) delivery.
He threw a 92-95 somewhat sinking fastball only. He had absolutely no command or control of it, and that did not look like a fluke to me (it could have been I suppose). It did not look like he had a lot of “life” or movement on the fastball other than some slight sink. If he has any secondary pitches, I would have to assume that he can rarely use them since he is so often behind in the count or if he does get behind in the count he cannot simply throw the fastball for a strike when he has to.
The absolute, number one thing a successful pitcher has to have, almost bar none, is command on his pitches, at least the fastball. Without that, unless you have a ridiculous fastball in movement, velocity or both, you cannot be a successful pitcher. The reason is obvious but I’ll spell it out. First, you will walk too many batters, second, you will make too many mistakes with location, and third, batters will be able to guess fastball and location because you are behind in the count too often.
Why would the Cubs ever think that this guy is even close to being an ace reliever? Or maybe I am wrong in my assessment. I would not let this guy pitch in the majors yet until if an when he gains some control/command.
Friday, April 20, 2012
Great idea by Jeff. What we really care about of course is their fastest speed. Anything slower than their fastest, and we can presume there was some non-100% effort.
Just thinking about 100m runners, and I’m going to guess that going all-out means that they’ll run at +/- 1% of the top speed. So, someone who runs home to 1B in 4 seconds will run it at 3.96 to 4.04 seconds if he always goes at top speed. Something like that, as a guess. Maybe +/- 2%, but I don’t think that wide a range.
Friday, April 13, 2012
Great stuff:
Tuesday, April 03, 2012
http://tangotiger.net/survey/
If you haven’t participated, please do so. I’m especially looking for Rockies fans. And, a few more Marlins and A’s fans would be nice.
Tuesday, March 27, 2012
Do your magic, and then spread the word.
http://tangotiger.net/survey/
Monday, March 19, 2012
Exactly:
“If you need 2,000 plate appearances to feel comfortable about a player and I can do it in 1,000, while also augmenting it with scouts I trust, that’s my competitive advantage. So in some respects, the new competitive advantage actually gets back to traditional scouting.”
Two guys can have the same +20 UZR in 130 games. But then I tell you that one guy can run 90 feet in 3.3 seconds and the other runs it in 4.3 seconds. What is your estimate at their true talent level? It becomes a Bayesian problem, and you use their speed as a prior. Without using speed, you may think both are true talent +10, and they both got somewhat lucky. But, use speed, and then maybe the fast guy is +14 and the slow guy is +6. (All numbers for illustration purposes only.)
But, say those guys are +20 per 130 games, but you have 520 games worth of data to base it upon. Now what do you do? Well, now you might end up that the fast guy is a true +16 and the slow guy is a true +13 or something. And if you had 1500 games, then the fast guy is a true +19 and the slow guy is a true +18.
Basically, there’s only so much scouting can give you. And eventually, given a large enough sample size, it trumps all. The problem is knowing how much sample size you need. And the other problem is that players are human, so they learn, adapt, change. It’s a delicate balancing act.
The biggest problem with scouting is when they are influenced by the outcome numbers, that the scout has a need to “explain” the numbers. You don’t have that much of a problem in the other sports, because those other sports don’t have a number for everything the player does. So, the scout won’t be biased, because he has no information that will bias him.
Friday, March 16, 2012
Great stuff from an old-time scout, using the experienced approach, but being given the data (with a shout-out to Dan Fox). This is a great example of how there’s very little separating scouting observation and performance analysis. It’s just a matter of having access to the same data, and that data is the bridge. Without the data, one side is stuck in NJ and the other is in NY.
Friday, December 09, 2011
Matt follows up John Mayne’s article from almost two years ago, with his article.
I’m not really surprised by the results. However, I’d like to correct Matt’s interpretation when he says things like:
Not only do pitchers who throw faster succeed more often, but they improve more as well.
Let’s go back to what ERA and component ERA (like FIP) tells us: it is an INFERENCE of their true skill, based on the OBSERVATIONS available. That’s all these things are. If all I know is his K/PA, then I can infer how good he is, with a certain confidence interval. If I know his BB and HR and BABIP, then I can infer better.
So, if you have two guys who have an identical K, BB, HR, BABIP rates, but one also happens to throw 95mph and the other throws 90mph, then we can INFER that the harder thrower guy has less good luck in his ERA than the 90mph guy. It’s more real for the harder-thrower than the softer-thrower.
The job of the saberist is simply to figure out: how much more real. That’s the only job of the saberist, to infer as best he can, given the observations at hand.
Now, if the softer-thrower actually locates his pitches better, then that’s another variable to consider.
But, the important point is this: ERA is simply an observation, and observation that has a certain amount of good luck and bad luck.
It’s more obvious with things like steals per SB attempt. If one guy can run from 1B to 3B in 6.2 seconds, and another guy can run that in 8.2 seconds, and both guys have an 80% SB success rate, guess which guy got luckier? Now, obviously, you want to measure things like jump and reading-the-pitcher, etc. But, given a large enough group of players, say 20 in each, those kinds of things will likely cancel out. So, you’d bet on the faster guys to have a better success rate (since this will presume “all other things equal").
And, hard-throwers are more likely to perform better than soft-throwers, all other things equal.
Tuesday, November 01, 2011
Below you will find the results of the 26 players who played on more than one team in 2011, and who received at least 5 votes from each of their teams’ fans. Wilson Betemit for example received an identical (overall) score from both the Royals fans and the Tigers fans. You will see broad agreement for most players.
Those who didn’t get much agreement either points to a gap in the methodology, or perhaps captures some dissatisfaction with that player. Colby Rasmus for example probably embodies this the best, with a low score with the Cards fans (in 2011) of 44, while he got a 63 with Jays fans. Last year with the Cards, his fans gave him a 57, and in 2009 he was a 71.
Read More
This is the last week to submit your ballots for the Fans Scouting Report.
http://tangotiger.net/scout/
Saturday, October 08, 2011
Looks more like quantitative analysis at the scouting level. Good stuff.
Helping determine who those players are is a major part of Weisbrod’s job as assistant GM of player personnel. A Harvard University English grad with a fondness for the classics, Weisbrod played hockey, but a shoulder injury ended his minor pro career. He found administrative work in the IHL, where he ran the Orlando Solar Bears. The Bears owner, Amway co-founder Richard DeVos, liked Weisbrod so much he named him president of the NBA’s Orlando Magic.
Unfortunately for Weisbrod, he got off to a bad start with Magic fans. Instead of drafting Emeka Okafor, he went for Dwight Howard fresh out of high school. The result was garbage bags filled with hate mail, a sampling of the hostility that followed when Weisbrod traded Tracy McGrady to the Houston Rockets. For that, Weisbrod received hand-delivered death threats at his home.
“[McGrady] was one of the most talented players in the league, very popular, but I came to the conclusion he didn’t have the internal fortitude to win a championship,” Weisbrod said. “I went to the ownership and said, ‘He can be Robin, not Batman.’ The FBI moved me out of my house to a hotel under an alias [because of the public’s anger].”
Weisbrod resigned in 2005, returned to the NHL and later joined the Bruins, where he was hired as a scout by former Harvard teammate Peter Chiarelli. Impressed by Weisbrod’s résumé, Feaster got permission from the Bruins to talk to him. That was before the 2011 playoffs began. Once the Stanley Cup was secured, Weisbrod listened to Feaster and saw another chance to redefine a franchise.
Thursday, September 29, 2011
A few years ago, MLB was filled with great fielding 3B. Lately, however, things have started to change back. I looked at the Fans Scouting Report, to see how the fans evaluated their team’s 2B and 3B. With only three teams did the fans strongly prefer the fielding talents of their 3B over their 2B:
Cleveland Indians
St. Louis Cardinals
Atlanta Braves
On the flip side however, there were 9 teams that strongly preferred their 2B to their 3B.
And how about on offense? Well, the shift has been so dramatic that the average 2B is a slightly better hitter than the average 3B! Just a few years back, MLB not only had the better fielder at 3B, but also the better hitter at 3B. Now, in 2011? A better fielder at 2B and just a shade of a better hitter at 2B, too!
***
How about LF/RF? I noted in the past that the better fielder was by far in RF than LF. Is this still true in 2011? Only 1 team had the clearly better fielder in LF than RF (Yankees). On the flip side, there were 13 teams with the better fielder in RF than LF (with 8 of them being in the NL, a league that doesn’t have a DH, and so, may use LF as a DH-like spot).
How about hitting? Not only is the RF a far far far better hitter than the LF (average RF is a slightly worse hitter than the 1B), but EVEN THE CF is a (slightly) better hitter than the LF. The LF in 2011 is clearly the spot where teams “hide” or otherwise “play with” their players.
Never do what others have done in the past, and that is to “zero out” the stats such that the average LF (offense + defense) is considered equal to the average CF or average RF. This is clearly the wrong thing to do.
Congratulations to Troy Tulowitzki, on being voted the best fielder in baseball, by the hardcore fans who visited Tangotiger.net.
Voting will remain in effect until shortly after the World Series.
Tuesday, September 27, 2011
I’m going to make one post like this a day, focusing on a team that requires more participation.
Today, it’s the Dodgers. If you follow the Dodgers, have a Dodgers forum you frequent, know where the Dodgers fans congregate, then send them my way. Or post a link below, and I’ll post in that forum.
http://www.tangotiger.net/scout/
Monday, September 26, 2011
I’m going to make one post like this a day, focusing on a team that requires more participation.
Today, it’s the Nationals. If you follow the Nationals, have a Nationals forum you frequent, know where the Nationals fans congregate, then send them my way. Or post a link below, and I’ll post in that forum.
http://www.tangotiger.net/scout/
Monday, September 19, 2011
While I have no qualms with his basic point, his conclusion misses the larger point. If it is impossible for Jason to evaluate Mark Ellis and Luke Scott’s throwing, then this only invalidates the results of the data if you end up comparing Mark Ellis to Luke Scott.
HOWEVER, and this is important, this does NOT invalidate comparing Ellis to Brandon Phillips. If let’s say everyone is having a hard time following the instructions, then this bias applies to all secondbasemen, to some similar degree. Which is why getting 20 or 30 evaluators for each player is important: if my instructions to insist on position neutral is too complicated, then those instructions are NOISE. (Random noise, within each position.) And how do your counter random noise? With sample size.
So, it works on two levels:
1. If you believe that fans can go the job on a position-neutral sense, then you get great results.
2. If you don’t believe they can do that job on a position-neutral sense, then random noise of the instructions is reduced by sample size, and you get great results (if you stay within position).
Jason ignores the FSR for whatever reason. But, if you actually look at the results, are you left scratching your head thinking “that’s totally off”? No, you don’t. Well, you shouldn’t in most cases. Indeed, I’ve asked a few teams in the past to evaluate their players (and they do follow a position-neutral aspect to it, as they should). And guess what? They always say the same thing: most of it looks really good.
Tuesday, September 13, 2011
By , 03:22 AM
I know there has been similar research published on the web, but I am too lazy to look it up right now. I took the 1998-2010 draft list from BA and looked at how the various pitchers did in the major leagues, breaking the rounds down into various buckets. It was a quick study and I just matched names from the draft lists with my major league databases. I probably missed 3-5% of the players because the names, especially first names, did not match up exactly.
First I looked at the rookie years for all pitchers in each draft round 1-3+, for a total of 4 buckets. Each round included the supplemental rounds, so, the first round actually has 60 picks in most years. The rest of the rounds pretty much have 30 picks each. Perhaps I should consider the first 30 picks of the 1st round as the 1st round and then the next 30 picks in the 1st round supplement as the 2nd round, etc.
Does anyone know if there is anything special about the supplemental round after the first 30 picks or is it just the next 30 best players and then the second round is 61-90 best players?
For each pitcher, I looked at whether their primary role in their rookie major league season was as a starter (S), a reliever (R), or mixed (N).
The currency I used was my normalized, component ERA (nERC), which is an “ERA” based on a pitcher’s raw stats (s, d, t, hr, nibb, hp, outs, and wp) adjusted for park, opponent, and defense, and normalized to his own league, where the average pitcher in each league, weighted by TBF, is 4.00. I weighted the aggregate nERC in each bucket by each pitcher’s IP. If I used the simple average of all the pitchers (weighting each pitcher exactly the same regardless of how many IP they threw), the numbers would be much higher, as the pitchers who had the worst true talent generally had the fewest IP.
Read More
Monday, September 12, 2011
Great piece by Keith Isley.
Sabermetrics evaluates a player’s “true” abilities as statistical regularities found in large samples. Defensive evaluation is the one aspect of the game that has been resistant to the statistical approach, at least so far. The customary sabermetric solution to this problem is to get more data and treat it with stronger mathematics, which is exactly what the advanced PBP-based defensive metrics do. Sometimes, though, progress requires not simply more data and better processing, but new methods that produce new observations that lead to new abstractions.
Fortunately, there is an established (if not well known) framework for the objective and rigorous analysis of subjective data. It’s called Q methodology, and it’s one of my favorite tools for understanding a phenomenon from a different perspective. Q provides instrumentation (the “Q sort”) for the quantification of subjectivity and a technology (factor analysis) for data reduction and interpretation.
A Q sort ranks orders subjects by a series of relatively subjective variables (such as those typically provided by baseball scouts) and the factor analysis uncovers the commonalities among those subjective elements. The result is a better understanding of what it is that makes a fielder great. Let me run through an example, and I’ll put the more technical details in a footnote.
Note: that article was from Feb, 2006. I took a two year blogging hiatus between Apr, 2004 (my old blog at Primer) and Jun 2006 (when I started blogging here), between which, I worked on The Book. I confined my comments to threads at the old Fanhome/Scout otherwise (for all intents and purposes, inaccessible now).
So, I happen to run into the above article, and, since there’s no active thread for it, I figured it would be a good one to link to. So, if you happen to see me link to more old pieces between those two dates, will, just treat it as something new.
Tuesday, August 23, 2011
The ninth annual ballot is now ready!
This is my single favorite project that I’m involved in, and its success is completely dependent on your participation. Your help benefits everyone out there.
http://www.tangotiger.net/scout/
And help spread the word on your blog!
Wednesday, May 11, 2011
Perfect, just perfect.
Glove-slap: Neil.
from Amazon
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