Archive for the ‘statistics’ Category
A while back I was harping on about the extreme variability of fielding statistics, which pretty much came down to a big difference in the data collected by Baseball Info Solutions and STATS. The example I used was Troy Glaus, who 0ne company says is one of the best defenders in the league and the other claims is one of the worst. Well, I haven’t even looked at anyone other than the Jays, and here’s another example…
Despite winning a Gold Glove for the last three seasons, Vernon Wells never gets great marks from the defensive metrics (especially in 2005, when his fielding percentage was 1.000 but no matter who you ask his range wasn’t great). This season the numbers from BIS rank him as the worst centre fielder in the American League. He has a terrible Zone Rating, and the fewest balls caught Out of Zone (OOZ) than anyone in the league, most by a large margin.
STATS, on the other hand, says he was one of the best. Breaking down the numbers, STATS says Vernon got to 317/348 chances, while BIS gives him 289/329 on Balls in Zone and 32 OOZ, for a total of 321/361. Wells had 321 putouts this season, so that makes sense for the BIS numbers- I have no idea how he could get more putouts than chances under the STATS system.
So at least in this case, it looks like the CF Zones are smaller than the BIS ones- the opposite as it was for Glaus at third. Again, we could do a lot of work looking at retrosheet for the positions to try and figure out why one system has him making more plays and even compare the results for every player to figure out how much of a difference there are between the zone definitions, but I’m more interested in baseball than trying to statistically analyze what some company won’t tell me about why its data is right or wrong.
Again, I ask the question: even though the complicated metrics dissect and manipulate the raw data taking into account every possible factor (how hard balls are hit, from which side of the plate, etc.) how can we consider the rankings they come up with particularly useful or accurate when the two main sources of raw data keep their methods under wraps and reasonably often differ 100% on their evaluation of a player?
Fielding statistics are the cutting edge in baseball statistics right now, full of complicated math and constant developments. There’s been a lot of progress since fielding percentage was the best thing out there. First came Range Factor, a simplistic but surprisingly effective way of looking at a player’s range based on how many times they touch the ball. But the big revolution was when STATS started manually (3 staff members record every game independently) tracking every batted ball and recording where it went based on a number of zones.
There have been a ton of defensive metrics developed since then (PMR, UZR and +/- are the most popular), but the data they crunch is all gathered in the same way. There are now two companies, STATS and Baseball Info Solutions (BIS), that collect play-by-play data and sell it for thousands of dollars to teams and organizations. Here’s an article talking about the difference between the two and how much they correlate. It mentions that:
During this year’s MIT Sloan Sports Business Conference, Rob Neyer told attendees that the evaluation of major league player hitting, pitching and fielding performance has been adequately addressed, and Bill James agreed with him.
Ok, so we’re done, right? Problem solved? Hardly! While I agree that the systems are ingenious, you don’t have to poke around much to find some huge inconsistencies between the two sources of data they’re analyzing, and it’s not the sort of thing that you can get around by comparing or weighting both sources or multiple systems. I was hoping that I’d totally overlooked something, so I sent in the following question to the Hardball times (ignore my typo, I meant RZR, and they returned the favour by calling me Jonathan G.)
I have a question about UZR. A lot of sites have Troy Glaus’ zone rating at .737, which is the worst in the American League. That makes sense seeing that he has been hobbled by plantar fasciitis this season. However, Hardball Times has his UZR at .706, which is among the best in the AL. He also has more balls fielded out-of-zone than most players, which makes his range look like the best in the AL other than Brandon Inge. I thought that UZR was just ZR separated into two different components. How could it give such a different impression of a players’ range?
- Jonathan G.
I was rather disappointed that the answer wasn’t just that I was being an idiot. As the Hardball Times said in their reply, the difference in Troy Glaus’ zone ratings is due to STATS and BIS (ESPN uses STATS, the Hardball Times uses BIS) recording very different totals for both the number of balls hit into Troy’s zone and how many he fielded; enough to swing his ranking between the second-worst and the second-best third baseman in the league (now, as opposed to when I asked the question originally).
First, the two companies have significantly different definitions of the size of a player’s fielding zone. STATS gives him a total of 281 chances, while BIS shows 204 balls hit into his zone and 48 plays made outside of it, for a total of 252. That’s a difference of 10%, but the zone doesn’t have to be that much larger; it makes more sense that it’s only a little bigger but a lot of balls were hit just outside BIS’s zone, because under their system Glaus leads the league in balls fielded outside of zone and wasn’t exactly known for his diving plays or lightning-quick first step this year.
As long as everyone used the same zones, using larger ones wouldn’t make a difference for figuring out a player’s relative ability. However, the two systems also differ by 15 on how many plays Glaus made, with STATS crediting him with 207 plays (281 chances, .737 ZR) and BIS 192 (144 in zone, 48 outside of it). So who’s right? In the article by Sean Smith mentioned in their reply, he points out that most putouts (which are mostly fly balls, line drives, etc.) don’t count as “plays” for the purposes of Zone Rating (unless a player fields a grounder and steps on the bag). Troy Glaus had 197 assists this season, so according to STATS he made an additional 10 plays by way of the putout. However, according to BIS, he completed 5 fewer plays than assists. In that case there had to be some unusual assists, such as a deflection to John McDonald that would give Glaus an assist but not credit for a play.
Either there’s a lot of human error, or there’s a really different definition of what counts as making a play. The BIS number is closer to the number of assists, but having watched Troy limp around out there and gaze wistfully at balls he would have dove for last year, I find the excellent ranking given by their system a little suspect, especially the huge number of balls ‘out of zone’ (48) he got to. But who knows? That’s one of the problems with secret, proprietary statistics. Unless someone has 10-20 grand lying around to delve into the raw data, there’s no way to know or to break it down and see what’s causing the difference or whose system could be leading to inaccuracies. And if the only two sources for play-by-play data available can report a player on absolutely opposite sides of the fielding spectrum, how can you take the results of all the fancy analysis based on them seriously? GIGO.
As I mentioned last week, Jesse Litsch has been all over the place as he learns on the mound this year. Fortunately, the good people at pitch f/x managed to capture his latest gem against the Yankees in all it’s glory, so we can dissect it to see what he’s figured out to get over his late-season slump.
Here is a graph of the movement on his pitches that befuddled the Yankees. As could be expected, it looks a lot more like his gem against the Mariners than when he was getting shelled. His changeup is back, and although it’s not dropping as much consistently, he’s throwing it more often than he ever has.
Also of note is that there isn’t much difference, if any, between his two big breaking pitches any more. This is good since his cutter/sinker/whatever has progressively started cutting further down and in this season. It has as much movement as his slider which almost makes that pitch redundant. Instead, Litsch is concentrating on a breaking ball that is halfway between his slow looping curve and faster slider, and throwing it as often as he was those two combined.
He was also getting his curve over for strikes low in the zone, and trying to back-door the Yankee’s left-handed hitters with it. Surprisingly, a lot of hit cutters ended relatively high in the zone, which is why he did get hit hard but for a lot of ground balls. He did manage to keep his tailing fastball low and away.
All in all, a nice way to end the year for the 22 year old – not just because he shut down the best offensive team in the majors, but because he did it with a return to consistency and a slightly streamlined approach that could help him continue that going into next season, wherever he ends up.
I’ve been rather obsessed with enhanced pitch data lately. The folks at pitch f/x took the wind out of this post by only getting it running 7 innings into Jesse Litsch’s impressive rebound start against Boston on Wednesday, but since they’re freely providing all this data, it’s hard to complain.
Specifically, I have big plans for a pitch database, and I’d to take a moment express my undying admiration for people who go out of their way to share their ideas and hard work. It sets a precedent for an open research community, and that sort of collaboration can set great things in motion. If I can ever figure out how to get it working on a Mac, I’ll be the first to give something back…Anyway, back to baseball.
After being one of the Jays’ best pitchers in his second Major League stint, Jesse Litsch, a.k.a Howdy Doody, went through a bit of a rough patch. His explanation was that his changeup was struggling. Interesting, but from watching the games, it looked like that wasn’t all. A quick look at the data shows what happens when you’re 22 still learning your five different pitches. You thought A.J. Burnett was variable? Jesse has totally different stuff from one day to the next.
1) The Gold Standard
One of Litsch’s best starts this season came on July 25th against the Twins. Although it ended in a 13-1 blowout, it was actually a pitcher’s duel until the bottom of the 6th, when the Jays put up 11 runs. Litsch had a 14-2 GB:FB ratio and only used 82 pitches through 7 innings.
The following chart shows the movement on his pitches, with speeds by color. As always with this data, keep in mind that dead center would be a ball that had no spin on it. His fastball is sinking about half a foot more than a Burnett 4-seamer.
I’m not going to get into release point and location unless anyone is really interested…just look at the difference between that and the next example:
2) Jesse Gets Shelled
On September 3rd, Litsch lasted only 3.1 innings against Boston, and gave up 7 runs on 7 hits. It wasn’t pretty.
Is this really the same pitcher??
- His changeup has zero movement and he gives up on it.
- He’s not throwing his sinker as hard. It’s dropping more, but doesn’t have the same tailing action.
- He’s throwing his cutter harder than his fastball now, and heavily relying on it (Baseball America’s knock on him in their scouting report)
- His curve never made it out of the bullpen
- His slider isn’t there either (he was actually overthrowing it and half of those green dots you see low are it bouncing towards the dugout).
If I had to take a guess, I’d say he was trying too hard to keep his sinker low against a tough team, and was overthrowing his cutter to the inside part of the plate and falling behind. But who knows…maybe he just came out and didn’t have a feel for anything. Let’s bring out our next contestant…
3) Losing to freaking Baltimore
This is Jesse’s latest start against Baltimore. Note that he came out firing the ball – his fastball was around 92 instead of 88-89. It didn’t help much, he was gone in 3 innings, allowing 7 hits and 4 runs to Baltimore.
What’s the problem? His changeup seems to have recovered somewhat- it’s a little faster, but moving again, and 10 mph slower than his fastball. His cutter is cutting in more and more every start, and he’s throwing a breaking pitch with some regularity again. Unfortunately, of Jesse’s 15 fastest pitches, 10 were balls, 3 were fouled off, and two were called strikes. All the contact was made off his cutter and slider because Baltimore was sitting on them since he couldn’t get his newfound heat over for strikes.
It’s hard to get a handle on what Litsch is doing on the mound when it changes with the weather. I don’t think these graphs say a lot about what kind of pitcher he is, but they say a ton about what happens when you’re thrown into the big leagues straight from AA. This is why he hasn’t won the 5th spot in the rotation despite an ERA (3.62) in the two and a half months since he returned to the big leagues that you would dream of having there. He has the stuff to be a decent starter, but he’s piecing it together as he goes along and still learning how to be consistent- and that’s usually what AAA is for.
This time it’s mlb.com itself having a little trouble with their fact checking:
Road worriers: The Blue Jays have compiled an impressive 35-22 at Rogers Centre, but have stumbled to a 23-35 mark on the road. That is largest discrepancy of home and road records in Major League Baseball.
It’s not even close: the Milwaukee Brewers are the biggest homers in the league (for a complete rundown, see my in-depth and evidently boring-as-heck article on this topic. If you’re not quite ready for naptime, battersbox has a fantastic historical analysis, and with pictures, too!)
|Team||Home Record||Away Record|
|Toronto Blue Jays||35-22||24-35|
Not only do they have a significantly better record than the Jays at home, but their record is even worse on the road. The Blue Jays are just at the high end of the spectrum….Milwaukee is on pace for a second straight year of the highest home field advantage not at mile-high stadium in the last half decade. I have no idea why- it’s a pretty normal, predictable park with a lot of room in foul territory. And just like the Jays, they hit a little better, but more importantly their pitching is vastly better, with more than 1 run a game less allowed at home.
So the Blue Jays finally made it to a five game winning streak, their first in 182 games (despite what the Star thinks). They also have a chance for their first 6 game winning streak in 555 games, and that looks possible considering they have won 5 of their last 6 games with Friday’s starter, Josh Towers, on the mound. And then comes the Doc, so who knows where this insanity will end…
Continuing my recent trend of reducing the intricacies and beauty of baseball to a few not-entirely representative columns of data, I decided to calculate the probability of either of these two droughts occuring. If you really want to put yourself to sleep with the math, be my guest. The results are:
Probability of a team with our record not winning 5 games over a 182 game period: 3%
Probability of a team with our record not winning 6 games over a 555 game period: 1.4%
There are a couple of baseball-ignorant assumptions made for these calculations. Although we have a .506 record since the last 5-game winning streak, of course that doesn’t mean we had that chance to win every game. Teams go hot and cold, home and away, face winning and weaker opponents. But those are actually more likely to group wins and losses together.
The real streak-breaker is having a terrible 5th starter. Although the 2006 Josh Towers was not in the rotation for the 5-win drought (the last time we won 5 in a row was the week we dropped him from the rotation), the back end of our rotation has been a black hole for a while now (11-32 last season). Having really good and rather bad starters instead of constant mediocrity will make it harder to put up streaks – e.g. if all your starters are .500, your chance of winning the next 5 games is about 5%. Having the same record but with a 75% starter and a 25% starter lowers your chances to 2%.
Unfortunately, none of this means that we are any more likely to win tomorrow due to the “law of averages.” As much as it pains me to reject any assistance, however mythical, to the Jays continuing to win games. Heck, if the Easter Bunny wants to help keep them on a roll, I say we pencil him in at rover…
I took a stab last week at comparing a team’s consistency in scoring runs to the style of ball they play. The result was a little counterintuitive- that last year, the Jays were more consistent than the best “small ball” teams, despite the idea that those teams can scratch across a run here or there when we need it, while the Jays sit back and wait for the home run.
I had a few requests to run the standard deviation for this season, so I did it for a few more teams this time:
I though the Jays would be higher this season because in May they hit .230 and slugged 40 home runs. But they’re pretty much where they were last year, which is at the lower end of the scale. And again, speed has no particular effect on the results.
I might be convinced into doing more teams if anyone can think of something (other than total runs scored) that might correlate with more consistent scoring, but for now I’m happy with the results:
- The Jays have not been an inconsistent team this year or last;
- Having a running game does not even out the runs you score.
Another myth the projections blow out of the water is that a manager’s in-game strategy means a lot. You don’t have to go to many street corners to find someone willing to venture that John Gibbons has cost us “at least 10 games” this year, like that time he pulled that pitcher, or didn’t pull that pitcher, or went with his gut, or played the numbers, etc. Here’s an article from Jay’s Nest that says “Gibby has personally lost about 6 or 7 games this year alone–despite injury.”
Using the same rationale as clutch hitting (below), ALL the effects not directly related to the team’s ability to score or prevent runs have added up to less than two games over the entire season. There is simply not a manager in the league who could squeeze a 55-43 record (7 more wins) out of a team that has scored 9 more runs than they’ve given up.
In fact, looking at two universally lauded managers- Jim Leyland and Tony Larussa, it’s interesting to see that their teams have exactly the records that their runs for and against predict, despite the notion that they can conjure up wins out of thin air.
There’s still a lot they might be doing to help their team score more runs in the long term (such as nurturing players and putting them in positions where they are more likely to perform) and the short term (shrewd pitching matchups, defensive alignments) – but as a rule of thumb it takes 10 runs saved/added to equal a win, and that’s a lot of good moves to have a noticeable effect.
Usually when people complain about a manager it’s usually for not coming up with the right move in a specific situation that would have won/saved a close game. And those almost entirely even out- no manager defies his team’s numbers.
Ok, last post that has anything to do with pythagorean projection, I promise. I’ve just started to see the game in an entirely new light because these things are so eerily accurate. The inevitable conclusion is despite all the agonies we go through in terms of individual situations and strategies, nothing really matters except how many runs you allow and score over the course of a season.
Especially after this last terrible series against New York, a lot of commentators (including most recently, DJF) have concentrated on the Jays’ lack of situational hitting for the season’s woes. As I pointed out in a previous post, going into that series they were hitting significantly better with runners on or in scoring position. Even after that hideous bout of stranding runners, they still hit better with runners in scoring position (.266) than they do without (.259).
The damning stat often quoted is that the Jays are .222 with runners in scoring position with 2 outs, which is a fluke stat that doesn’t mean anything. Why does it matter how many outs there are when you fail to bring someone home? The Jays are also hitting .301 with RISP and less than two out- do all those runners that we scored at an impressive rate earlier in the inning count for less?
It’s because as fans, we put more value on hitting with 2 outs because it’s the last chance to drive in those runs. We totally forget about Glaus whiffing madly at a pitch that bounces before the plate if Frank hits the first pitch he sees for a home run. But if Glaus had done his job, the end result for the team and the game would be exactly the same. There’s also the idea that with 2 outs, the situation is more likely to be “clutch”, meaning the game is on the line and therefore those at-bats matter more. But clutch hitting (i.e. hitting better in high leverage situations) doesn’t exist. It’s just random fluctuation, and even that has little effect.
To see this, take a look at the Jays win/loss projection so far. If they were hitting worse in game-changing situations, then they would have won fewer games than their projection (which is by definition for a team that scores runs randomly). The projection is pretty easy because they have scored almost exactly as many runs as they have allowed, and gives a win % of .502. That’s 47.5 wins. The Jays currently have 46 so ok, they’re under-performing by a whole game and a half.
It would be different if they were hitting poorly with RISP- then you could argue that they weren’t driving in as many runs as they should be. But they’re doing that just fine. But if you’re arguing that the Jays hit the ball better when it’s meaningless, but choke when it matters (may I suggest the “close and late” stat- they’re hitting .235), keep in mind that if true, at an absolute maximum the effect has been between one and two games this year. That’s not the reason the Jays are 10 games behind Boston- hitting under .260 as a team is.
The last post was meant to show that method of projection has some sort of validity over the long run – but it’s useful for things other than hindsight and moaning about what could have happened in a season. Although it gets more prone to error over the short term, we can also use it to estimate a pitcher’s record based on their ERA and run support. This has a few uses:
1) Determining whether or not a pitcher “just knows how to win”.
You know the old chestnut- a certain pitcher might not pitch that well all the time, but he’s able to do just enough to get the win. Jack Morris is the archetype; the Blue Jays Version is Gustavo Chacin, with his career record of 25-15 despite a 4.18 ERA.
Unfortunately for the little person inside you who has watched too many movies and just wants to believe in gutsy performances and brave stands, this phenomenon never holds up to analysis. Gus allowed 93 runs in his career year of 2005, and was given a stunning 140 runs in support. Using these numbers, he was projected to win .693 of his decisions that season, which would have given him a record of 15-7. He actually finished 13-9. In other words, he actually figured out how to win less than he would have if the runs he allowed were randomly distributed that season.
2) Isolating run support vs. dumb luck
We all know that a pitcher’s wins are a pretty useless way of determining how they’re performing, but they’re never going to be abandoned because they provide a broad, easily digestible at the season so far. But we can adjust them to isolate two factors:
- Dumb Luck: by comparing the pitcher’s actual wins to how many they were projected to have (based on the same runs scored and allowed), you get an idea of how much they have been helped or hurt by the distribution of said runs (which is for the most part random). Lets call that EWins for “expected”.
- Run Support: Similarly, comparing how many wins a pitcher would get with the team’s average run support to the number of Ewins he got with the run support they gave him (I’m using Ewins instead of actual wins to eliminate the luck factor), you get an idea of what sort of effect the support or lack thereof of the team has had on a pitcher. Let’s call that N wins for “normal”.
Here are the Blue Jays Starters this year, adjusted for luck and run support:
I really like Nwins. Saying that Roy Halladay has received an extra run and a half per game doesn’t really mean much to most people. Saying that because of that he’s won about 3 games he would have otherwise lost does. Of course this is particularly useless for the guys who haven’t had a lot of decisions (especially Marcum), but it does show the extent to which Roy Halladay has been bailed out this season by the offence, and the fact that despite the sensation that AJ has been pitching better than his record, his W-L is right where it belongs.
3) Figuring out how well a pitcher would do on a better team
Let’s take Dan’s example of Matt Cain from a previous post (check out the blow-by blow of his torturous season in the comments). He is now a ridiculous 3-10, but his losing record is not so much the fault of the Giant’s woeful offence as it is with luck and him getting runs at all the wrong times. His record should be .500 this season because he’s allowed as many runs as he’s received in support. It would rise to 7-6 if the Giants scored their average number of runs for him, but it would only make it up to 8-5 if he played for a league average team (like the Blue Jays).
Going 3-10 has only been possible because of the insanity-inducing pattern the runs have been scored in, and that won’t last in the long run. But still – Matt Cain right now is a .500 pitcher for the Giants, but would be in line to win 18 games for the Tigers.
It doesn’t take a lot to see the problem with the Jays offense. The team is hitting a collective .257, when last year hit .293 over the first half. Thirty-six points of average across the board! Imagine you had a lineup that hit .300 from top to bottom and then the next season they all dropped down to mediocre .264 hitters.
From top to bottom, every player except for Alex Rios is hitting below their career averages. Glaus’ average is up, but he’s missed a lot of games and his power numbers are down. This is why management continues to insist that the team will eventually hit once it’s healthy. Barring more injuries, it is incredibly unlikely that the entire lineup will continue to underperform over the entire season.
However, it is fundamentally against human nature to be able to accept that these sorts of things just happen, and you have to wait them out (unless you are a professional poker player in which case you live and breathe this concept). And so, theories abound as to what is wrong with the Jays. One of the most popular is that we don’t steal enough, even though all of baseball discovered long ago that the effect of the SB was hugely overrated and it has dropped off the map for good reason.
Another is that we rely on the HR too heavily: the “all or nothing” theory. The Fan 590 published an article today by Michael Hobson that made my head spin. The author claims that we would be better off with hitters in the middle of the lineup who hit for average rather than the perpetual 100 RBI machines that are Troy Glaus and Frank Thomas. This is wrong on so many levels.
First, baseball analysts will tell you that clutch hitting doesn’t exist to any significant degree. You don’t have to look any further than their splits from the last three years to see that Troy and Frank‘s averages with RISP are right in line with their normal averages. (And as for “men on base”, the term the article uses- Frank is hitting the same as his normal average with men on base and Glaus is hitting much better (.301)).
Second, a power hitter will always drive in more runs in the long run. Yes, it hurts to watch them take huge cuts at a 2-2 pitch with the bases loaded when you wish they could choke up, but the fact that they strike out 100 times a season has nothing to do with their potential to produce runs. They also lead the team in OBP (which means they get out the least often) hit sacrifice flies at command, and on and on.
The entire definition of a power hitter is he will drive in more of the runners on base in exchange for doing it with slightly less frequency. There’s really no way to argue it- a team’s success is most directly linked to the number of runs it scores. If a player manages to drive in 100 runs, it doesn’t matter if they hit .150, they are greatly helping the team. The conclusion that it would be better to have two contact hitters in the 4-5 spots instead of proven RBI producers is absurd.
While I’m at it, the article also asks the rhetorical question:
This is a club that is at the bottom of the league in hitting with men on base. Why?
To which I can only be a broken record: Because they’re hitting .257 (21st in the league). In fact, they’re hitting better with runners on base, at .269 (18th), and even better with runners in scoring position: .272 (11th). I seriously doubt that they are “among the lead leaders in leaving men on base”.
Dan mentioned in the comments last week that better pitchers might get less run support because in the NL they get more at bats (and are less likely to be pulled in critical RBI situations). So I ran the same WHIP vs. Run rupport graphs for the AL vs. the NL. And he’s spot on:
The slope of the AL (blue) graph is negligible, and the entire effect comes from the NL (red). Wow, half a run is pretty serious. Maybe they should put some batting cages out next to the bullpen. Get off your asses, boys! Time to help your own cause occasionally…
Ok, we all know that a huge amount of cash is no guarantee of a World Series. Or even a passable team (Cubs, Rangers, etc). But it sure helps. Give any team the Yankees payroll and while they will still need to find some talent, they can shrug off any serious blunders (Pavano, Igawa) and smooth over any glaring deficiencies in their club halfway through the season (Roger), as well as being able to attract the best and the brightest from other teams come free agent time (Mussina, Giambi, Sheffield, Damon, Rodriguez, Abreu, etc…)
So, I propose a new stat- Wins/$. Let’s see who is the best right now in terms of how much cash they’ve sunk into this season.
AMERICAN LEAGUE EAST:
TAMPA BAY: 1.30
NEW YORK: 0.19
This is just a hilarious result. Of course New York and Boston were going to end up in the basement because they shell out such a ridiculous amount more than anyone, but even Baltimore who are in an absolute free fall, have fired their manager this week, etc, manage to scrape together the same W/$ as the best team in baseball? And of course the real winners are Tampa, who have patched together a similar season to the NY behemoth while paying their team minimum wage. Can you imagine the team they would have if their payroll was an extra 165 million this year? No, you can’t.
And of course, as mentioned before, for the cash the Jays have invested, even in this horrible debacle of a season, pound for pound we’re looking pretty good. It’s just not just fair to compare us to the monsters in this weight class, and we outclass Baltimore and would mash Tampa except they’d dry up and blow away.
AMERICAN LEAGUE CENTRAL:
KANSAS CITY: 0.43
So Cleveland really does rock, and last year was just a dumb aberration due to some back luck and growing pains. But they really do have a great record and are doing it efficiently, too. The only real surprise here is that Detroit isn’t the young and super cheap team you thought they were after fielding a AAA team for a couple of years. They have followed that up by shelling out the big bucks and have signed some big bats to push for the postseason now as well as some longterm contracts.
AMERICAN LEAGUE WEST:
LOS ANGELES: 0.42
It’s no big surprise that Oakland is among the most efficient, but did you really think the Angels were still underachievers? Or that TEXAS, the worst team in the league, was getting more for their buck than Seattle??
Do better pitchers get more run support due to the confident aura they exude? Will Josh Towers ever get any? I ran 2006′s data for starters’ (120 innings minimum) run support against their WHIP (walks + hits/innings pitched) to see if there was any correlation between the quality of a pitcher and how many runs his team scores for him. My first thought was there would be absolutely none, because that’s what modern mathematical analysis tends to do to vague psychological intangibles (clutch hitting, streaky hitters, protection, baserunner distractions, etc.) in baseball. The first results did not disappoint:
That’s about as randomly distributed a cloud as you can get; in fact the trendline has a slight slope that indicated worse pitchers getting more run support- though it’s not really enough to be statistically relevant. But there is an interesting gap: the pretty bad pitchers (WHIP > 1.45) in 2006 never got more than 4.5 runs. So I did another year:
Ok, the gap is gone- but there’s the same trend, and this year the slope is about twice what it was in 2006. Which might actually be relevant. So one more:
That’s three straight years in the same direction, and with even more of an noticeable effect in favour of worse pitchers. Stop the presses! Call SABR! Batters are slackers with the ace on the mound! Unfortunately, *slice* goes Occam’s razor – this can also be explained by other teams intentionally matching up their aces against yours (and the other way around), which is easily enough to explain a 1/2 run slope in your offence’s effectiveness without resorting to questionable psychological babble. This myth is busted.