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Parks
Monday, May 14, 2012
Phil looks at one component, to show how the relative rate of points scored by the home team is dependent on how “easy” it is to score per possession. The easier it is to score, the less the relative rate of points will be earned by the home team. The harder it is to score (the more things have to compound in order for a goal or point to score), then the larger the relative rate of points earned by the home team.
In basketball, the current rules have it that the home team ends up getting 52% of the points. But, Phil changes the rules so that more compounding actions have to happen in order to get points, and he can change that to 54% of the points goes to the home team. Or, he reduces the number of compounding actions so that the home team gets barely more than 50% of the points.
It’s still part of the overall theme of “confrontations”. For example, Nadal’s clay-court advantage is different if we just look at total points earned, as opposed to matches-won. If you count as a “win” each point earned, maybe Nadal wins 150 points on court, while losing 100 or something. That is, he gets 60% of all points scored. But, if you count as a win each game, then he might win say 70% of all games. If you count as a win each set, he might win say 80% of the sets. And if you count as a win each match, he might win say 90% of all matches.
So, you can change the rules, like Phil is describing, to control the home-site advantage. And the compounding effects of the confrontations is how you do it.
Friday, April 27, 2012
Well, this is another fun article that shows inspiration.
Sunday, April 08, 2012
By , 09:29 PM
Citi Field moved in some of their fences this year, and lowered them from 16 feet to 8 feet on the left side. Specifically, here are the old and new dimensions:
OLD NEW
Leftfield Line 335’ 335’
Leftfield 371’ 358’
Left Center 384’ 385’
Center 408’ 408’
Right Center 415’ 398’
Rightfield 378’ 375’
Rightfield Line 330’ 330’
Here are some curious quotes and numbers from this article on ESPN last year:
http://espn.go.com/new-york/mlb/story/_/id/7189410/new-york-mets-new-dimensions-citi-field-make-playing-field-more-neutral
An internal study by team officials determined the Mets would have hit 81 additional homers over three seasons had the new dimensions been in place. That’s an average of 27 per season, or one every three home games.
Visitors would have hit 70 additional homers over three seasons.
Citi Field allowed 1.33 homers per game last season, which ranked 14th of 16 National League ballparks, ahead of only San Francisco (1.00) and San Diego (1.23).
Team officials indicated the goal was not to tilt the ballpark in favor of hitters—just to make it more neutral. They apparently have succeeded in that regard.
Rybarczyk found that Citi Field should remain mildly pitcher-friendly with the revised dimensions.
Based on a scale in which 100 is perfectly in the middle of ballparks—and a higher number favors hitters and a lower number favors pitchers—Rybarczyk said Citi Field should now be a 92. That is still marginally pitcher-friendly compared with the average ballpark. The past two seasons Citi Field had a 72 and 74 rating, both highly pitcher-friendly.
That’s 151 more HR for 3 seasons or 50 more HR per year. Let’s assume that half of those were fly outs and half were doubles or triples. I have no idea what the balance should be.
That means that in the past, those 50 fly balls per year were worth 13.25 runs, using lwts values for an out and an extra base hit. If those 50 fly balls are now HR, that is worth 70 runs. So the difference is 57 runs. For 81 games, that is .70 runs. Over those 3 years, the run factor for Citi Field was .91. Are we to believe that the run factor is now going to be 1.08 (adding in the extra .7 runs per game), one of the best hitters parks in baseball?
Greg says, at least according to the article, that the “rating” (the article suggests that is a run factor, but doesn’t say for sure) for Citi Field the last 2 years was 72 and 74 and it will now be 92.
OK, Greg means the HR factor, but the article suggests it is the run factor. Anyway, I don’t know why Greg is quoting the 2010 and 2011 HR factors but leaving out 2009. Once you start computing and quoting component park factors, the sample sizes are very small, so you want to try and use as many years as possible.
In 2009, the HR factor for Citi Field was 1.04! So really, for the 3 years, it is .83. Greg says that it now should be .92, which makes sense I guess. An increase from .83 to .92 is around 10 extra HR per year for both teams combined, not the 50 that the Mets’ “internal studies” suggest.
Greg also says this:
If the new dimensions had been in place from the opening of the ballpark in 2009, Wright would have hit 13 extra homers over three seasons, according to Greg Rybarczyk, who operates hittrackeronline.com in partnership with ESPN Stats & Information. Bay would have hit nine additional homers at Citi Field over his two seasons as a Met.
That is 8.8 extra HR per season, just for Wright and Bay. Yet, Greg himself is suggesting that a new HR PF of .92, as compared to the old one of .83, would yield an extra 10 HR per season for both teams combined, as I stated above. Are we to believe that the new dimensions would have added 8.8 HR per year for those 2 players and 1.2 for everyone else, including the opposing teams? Those numbers make no sense either.
Maybe Greg can explain all of this…
Friday, April 06, 2012
Berkman thinks so based on two hard hit Stanton balls that didn’t go out.
Steve looked at it a few months ago.
Tuesday, March 27, 2012
By , 12:49 AM
It is universally assumed that a pitcher with a high fly ball rate (presumably with a high HR rate as well) will benefit from pitching in a pitchers’ park (especially if it is a pitchers’ park by virtue of a low HR factor), and that ground ball pitchers will benefit from pitching in a hitters park, relative to the average pitcher of course. If you were to Google so-and-so pitcher moving to San Diego or to Texas’ you would get thousands of hits relating to their fly ball or HR rates and the new park, and how it will likely help or hurt them. And of course this truism seems to make sense. But, as we continuously learn in sabermetrics, “making sense” does not the truth make!
For batters, the conventional wisdom is not as clear. I think that it is mostly presumed that a fly ball hitter will benefit from a hitters park and vice versa. Again, we mean over and above all hitters. Obviously all hitters will see their “raw” stats improve in a hitters park and decline in a pitchers park.
To try and answer some of these questions, here’s what I did:
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Wednesday, March 21, 2012
Jeff looks into it with some fascinating data.
Tuesday, March 20, 2012
Bill’s first of a two-parter.
Wednesday, March 14, 2012
Great stuff from Bill.
Note that the change in wOBA squared would equal to the change in PF. It puts both on the runs scale.
Wednesday, February 22, 2012
Good stuff from Bill, on checking to see how much we can rely on park factors, if they are hitter or pitcher.
Park factors is one of the things that’s bothered me for at least a decade, the way we use them as if it’s a fait accompli. Each park affects each type of player, and each player, differently. While using something for Coors and Petco rather than nothing helps more than it hurts, it still doesn’t mean that we shouldn’t do better. And when it comes to park factors, there’s LOTS of room for improvement.
Sunday, December 18, 2011
Too much is made of matchup stats (i.e., how batter X does against pitcher Y after 20-40 plate appearances, while ignoring each player’s career 2000 plate appearances).
At the same time, is too little made of individual player-park factors? If you have 3000 plate appearances of Barry Bonds at 3Com and away from 3Com (is that what it was called then? I can’t keep up with corporate names), and he hits as many HR at home as he does on the road, do we really care that the average LHH hits two-thirds as many HR at 3Com than away? I say: NO! Barry Bonds has very little in common with those hitters, other than handedness, but (traditional) park factors DEMAND that we treat Bonds as being tightly coupled with that group.
This is Mat Latos’s career Petco / non-Petco slash line (BA, OBP, SLG):
.229 .287 .348
.224 .286 .351
I won’t even bother to tell you which is Petco and which is not, since it’s the same thing! The question is: did he simply have made luck at Petco and good luck away from Petco? Or, is his talent level such that it’s an important parameter when looking at the park factors of the rest of the players? That is, with Bonds, he hits HR so far, that it doesn’t matter that 3Com suppresses HR.
Similarly, is Petco such that Latos can’t benefit from its pitcher-friendliness? I DON’T KNOW. But, these are the kinds of valid questions that are out there.
Latos’ strikeout rate per PA is 23.7% at home and 23.4% on the road. His walk rate is 6.9% at home, 7.9% on the road. His HR rate is the same 2.2% at home and on the road. His BABIP is .283 at home and .276 on the road.
Therefore, we need to do just like we do with handedness splits: personalize them. In The Book, we showed that you take the observed handedness splits and regress them toward the league mean (by adding 1000 PA for LHH and 2200 for RHH, to the number of their PA against LHP). We need to come up with the same thing for parks.
The problem is that you may have to come up with a different regression amount for each park AND for each quality of player. It makes it a tougher job.
So, we can’t presume that Latos was either:
a. completely unaffected by Petco (as evidenced by his splits)
b. extremely unlucky at Petco (as evidenced by all MLB players at Petco)
The truth is somewhere in the middle.
Tuesday, November 29, 2011
Can we really simply just do this, for Barmes?
Was Adrian Gonzalez disproportionately suited toward Fenway and against Petco? Clearly the opposing pitcher is going to try to mitigate whatever advantage a player has. If you just take Gonzalez extra base hits at Petco, and pro-rate to 351 PA, that gets you 27 extra base hits. If you look at all the other parks he’s played in, he pro-rates to 42 extra base hits in 351 PA. So, bring him to Fenway, and we’re thinking that he’ll do great right? He hit 31 extra base hits at Fenway.
Now, I’m not saying that Barmes at PNC will or won’t be affected. But there seems to be this presumption of overlaying performances, to see how someone will be affected. And, yes, I’m sure this is true… to some degree.
What a saberist’s job to figure out is the level of degree… how far can you make that claim.
Wednesday, October 19, 2011
Poz reels off some interesting info.
It probably helps to understand the home/away context. A team will score about 0.4 more runs per 27 outs at home than on the road. (This is why for example, they have a .540 win% at home: 0.4 runs = 0.04 wins, and 0.04 plus .500 is .540. Isn’t math fun?)
Another interesting thing to note is that a pitcher’s wOBA is around .160, while a DH is around .350 (more or less, and depending on the year). Pitchers come to bat 2.5 times, and then you have PH the rest of the way (PH are around .300 wOBA, and we’ll give them 1.5 PA). Remembering that to convert wOBA to runs you divide by 1.2, and we get: DH over P = (.350-.160)*2.5/1.2 = 0.39 runs. DH over PH = (.350-.300)*1.5/1.2 = .06 runs. So, the DH advantage is about 0.45 runs per game. Basically, the DH advantage is about as large as the home field advantage.
Ok, so he notes that:
On the road, with the pitcher hitting for himself, AL teams have averaged about 3.77 runs a game. With the DH, they average about a half run more per game. I think that fits in with what you would expect.
We have three effects: the home/away, DH/P effect, and the unfamiliarity of playing “out of position” on the road (AL team using a P, NL team using a DH).
So, being at home, AL would be +.4 more runs than on the road. Being at home, DH generates +.45 more runs than P/PH. Being at home, the familiarity of playing by your rules, you’d get some positive effect, say something like +.15 runs (just pulled it out of my a$$). The total of these two is +1.00 runs, but Poz is reporting only +.50 runs scoring at home for AL teams than on the road. There’s another 0.50 runs unaccounted for.
Anyway, we go on to the NL:
The National League, though, offers the shocker. At home, with pitchers hitting, they average 4.15 runs per game, which is pretty close to what American League teams score at home. But on the road, using the DH, National League teams have scored only 3.4 runs per game, meaning they score seven-tenths of a run LESS per game with the DH than they do with pitcher’s hitting.
So, at home, we’d expect them to score +.4 more runs. The DH impact would mean scoring -.45 runs at home (i.e., fewer runs at home than on the road). And then, some impact for familiarity at playing by home rules of +.15 runs. In total, we’d expect NL teams to score +.10 more runs at home than on the road. Poz is reporting that they score +.75 more runs at home than on the road. That’s an extra 0.65 more runs unaccounted for.
Is it possible therefore that there’s a huge familiarity factor of playing by your home league rules, way above the +.15 I was originally contemplating?
I mean just look at the data right here:
American League home record: 44-21 (.677 winning percentage)
National League home record: 37-29 (.561 winning percentage)
So, the average playoff team at home wins at close to a .620 clip, far higher than the .540 that you’d see in the regular season. And getting a +.08 win advantage, that’s a +.80 run advantage.
And that pretty much closes the gap in my numbers above.
That’s +.08 win advantage at home (over and above what you’d see in the regular season) is almost two standard deviations from the mean. It’s possible that this is just small sample size. But, seeing that we have a huge parameter here (playing by home-league rules), a .540 prior would seem to be too low to test against.
I’d love to see more data breakdowns and analysis on the AL/NL breakdown, including home-field advantage for intra-league playoff games as another comparison point.
Thanks Joe, learned something new today!
Friday, October 14, 2011
Tom,
I added the rest of the regular season data for 2011 and created the attached graphics to show the correlation between the sun’s position and hitting success. The big thing that jumped out at me was that hitting drops off when the sun is behind home plate and at an elevation of 20-29 degrees. According to the sketch-up model for Busch Stadium, shadows are cast between the mound and home plate when the sun is at a solar elevation of 21-29 degrees.
My biggest concern is that as you get to the extremities of the chart, fewer and fewer stadiums are contributing data points. I’m wondering if there are other park specific issues that could be impacting the results.
Let me know what you think.
Chris
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Tuesday, September 20, 2011
How this was allowed to happen in the first place is incredible. But at least it won’t happen next year.
Glove-slap: Dan.
Sunday, September 18, 2011
All data from 1993-2010.
I look at each player’s game, and captured whether they were in the starting lineup or not, and which park they played in. And of course, their wOBA. I matched on those categories (starting or not, park), to make sure that the player pool was equally represented in day and night. (That is, if Sosa had 1000 PA at Wrigley in the day and 2000 PA at Wrigley at night, then I counted Sosa with 1000 PA in each.)
For hitters in the starting lineup, their wOBA was .3429 in day and .3423 at night. Basically, a match. However, I then broke it down by “times through the order”. And that’s where things became interesting.
Their 1st time through the order, it was 0.331 day, 0.335 night. That difference, on 219239 matching PA, is 3.9 standard deviations from the mean. The 3rd time (and later) through the order, it REVERSED, with 0.347 day, 0.344 night, or 4.0 standard deviations (going the other way). If you think of day time as games playing from 13:00 to 16:00 and night as playing from 19:00 to 22:00, then it would seem that the optimal point to maximize offense is somewhere between 16:00 to 19:00.
Basically, the above data would be consistent with this idea. If you peak at around 5 o’clock, then comparing your 1st time through the order performance (at 1 o’clock) in the day to 1st time through (at 7 o’clock) at night, we see an advantage for the night game. However, your performance the 3rd and later time in the day (at 3 or 4 o’clock) to the same at night (at 9 or 10 o’clock) would give the advantage to the day.
Interestingly, for substitute players, we don’t see that! The first time they come up, their day and night performances are a match (.305 in day and night). This part makes sense, because they’d be playing at say 2 or 3 o’clock in the day and 8 or 9 o’clock at night. Equidistant let’s say from 5 o’clock. But, the 2nd and later time they come up, their performance in the day is .313 while it .329 at night, which is quite the reversal. (Based on 4417 PA, which is 2.1 standard deviations from the mean, but in the reversed direction of the starting group.) Maybe these guys are subs for some particular reason, maybe they partied too hard, and didn’t recover for day games? I dunno.
For pitchers, their performances are more consistent with the 5 o’clock theory. Relievers, who would be pitching between 3 and 4 o’clock, perform with a wOBA of .328 in the day. But at night, say after 9 o’clock, the wOBA drops to .324. With 270287 PA, this is 3.7 standard deviations from the mean.
As a general rule, if you need to visualize it, your wOBA is going to peak at around 5 o’clock (maybe 6? someone else can figure it out), and the wOBA is going to drop by a point or two for each hour from that peak time.
This has some serious implication for the “times through the order” effect, and may provide partial explanation as for why starting pitchers perform as well as they do the 4th time through the order.
Saturday, August 13, 2011
Interesting charts here:
This is non-Colorado pitchers, and we see they’ve decided to throw fewer sinkers at Coors:
But this is Colorado pitchers, and they’ve decided to throw more sinkers at Coors (though it could be that it’s not the same pitchers in the same proportion in the two groups).
And here’s the piece de resistance, comparing how pitches move at Coors compared to the league overall. The “eye” is where a pitch would be observed to be thrown if you were playing catch. So, a MLB fastball looks like it “rises” in a regular park in comparison, but at Coors, it moves “straighter”.
Wednesday, August 10, 2011
ESPN reports:
Now, by themselves, the above splits aren’t conclusive, so to measure the effect of Rogers Centre more precisely, The Mag consulted with Wyers. He has developed a method that generates park factors by comparing a player’s performance in any given park with his performance in all other parks, not just in road games for that player. This reduces statistical noise and offers a better estimate of how a park actually plays in a given season. Wyers found that for every ball that batters made contact with in 2010, Rogers added .011 home runs, up from a rate of just .002 from 2005 to 2009. That puts Rogers Centre in 2010 among the top 3 percent of home run ballparks since 1950.
But only the Blue Jays, and not their opponents, got a home run boost in Toronto. When the Jays were on the road in 2010, they hit home runs in 4 percent of plate appearances in which they made contact, compared with an AL average of 3.6 percent. At Rogers, their home run on contact rate soared to 5.4 percent, which is a home-field advantage seven times the magnitude teams typically enjoy.
Opposing batters, however, actually homered on contact at a below-average rate in Toronto. As a result, the power differential between home and visiting hitters at Rogers in 2010 was the third largest of any park in any season over the past 60 years (see chart).
I’ve never looked, but let me take a really quick look here. And sign stealing would go beyond just batted balls. Walks, strikeouts, the whole thing comes into play. So, let’s see what we have:
In 2011, so far, Toronto batters’ OPS is 69 points higher at home. Their opponents are 47 points higher at Rogers. Since the typical home field advantage is about 30 or 40 points, seeing a 23 point advantage for Toronto batters over their opponents actually works against the theory, but in reality, it’s well within random chance.
How about 2010? Toronto batters had a 64 points advantage at home, while their opposing batters are 3 points under, giving a whopping 67 point differential (compared to the standard 30-40). Is that a big deal though? That’s about a 15 point wOBA advantage on 3000 PA. One standard deviation would be 9 points, so we’re talking under two SD. By itself, maybe there’s something to it. Maybe. But couple it with 2011, and there’s nothing there.
2009? The Toronto hitters were 8 points UNDER, while opposing hitters were 63 points UNDER. Quite a reversal of fortune for all concerned. Anyway, that’s a 57 point advantage for Toronto hitters. Putting the three years together, the 57, 67 and 23 points of advantage averages out to 52 OPS points, compared to the league average of 30 to 40 points. That’s about 7 wOBA points. Given 8000 PA, one SD is 5.7 wOBA points.
I don’t see it. And if you were to do all 30 teams, I’m sure you’ll find a couple of other teams with a bigger advantage at home than the Jays hitters.
If anything, the outlier is 2008, before all this apparently started. The Toronto hitters had a 28 point advantage at Rogers. But opposing hitters were 50 points UNDER, for a 78 point difference, far ahead of all subsequent seasons. In 2007, it was 37 point advantage for Toronto hitters, while opposing hitters were 47 points under, for an even greater 84 point differential. In 2006, Toronto hitters had a 100 point advantage, while opposing hitters were 48 points UNDER, for a super duper differential of 148 points! You want to talk about something weird, then go back to 2006. In 2005, it was 70 point advantage for Toronto hitters, while opposing hitters were 4 point UNDER, for a 74 point differential.
So, 2005-2008 is where Rogers was the huge advnantage for the hitters, averaging 96 points of OPS advantage (compared to presumably a 30-40 league average, though at this point, I’m too lazy to look that up). Anyway, that’s about a 25 wOBA difference on 12,000 PA. With one SD being 4.7 wOBA points, that’s 5 SD from the mean. That’s about as big an outlier as you’ll ever find.
If you want to investigate, go back to 2005-2008, and find out what happened at Rogers. 2009-2011? Nothing in comparison.
Thanks to B-R.com for the ease of data access.
Thursday, July 21, 2011
Good stuff.
This analysis confirms a significant inverse correlation (more AF, fewer SO; less AF, more SO) between AF and SO, for the 3 time periods reported. It reaffirms the previously-reported relationship identified in the AL 1964-68, as presented at SABR 40 . Analysis of the effect of each square foot of foul territory on SO is currently in progress. It is hypothesized that the Cleveland Indians pitching staff would have SO more hitters in 1964-68 had they not pitched 81 games yearly in Cleveland Municipal Stadium.
Basically, the true relationship is between foul area and foul-strikes. If you have no foul territory, then those foul balls are out of play foul balls (and the at bat is extended). If you have a huge foul territory, then those foul balls are in-play foul balls. And if they are in-play, then they have a chance of ending the at bat “prematurely” (disproportionately early).
Strikeouts is what you use to infer this relationship.
Monday, June 27, 2011
On ESPN.
Thursday, June 02, 2011
Another honest mess. New park parameter should be used, but has not been handled.
There were 20 teams that played in 1968 that also played in 1969 (i.e., in 1969, it was the 20 teams of 1968, plus the 4 expansion teams). If we ONLY look at the matching teams (the two that played in both), we see that their per-game attendance increased from 14,220 to 14,760, for an increase of 4% in attendance. However, their won-loss record went from .500 to .527 (the wonders of expansion), and that kind of jump would demand an 8% increase in attendance. We can say therefore that attendance was DOWN 4% (4% minus 8%) relative to expectations of win percentage.
I did this for all seasons. The biggest year over year drop was 1994 to 1995: a drop of 20% in attendance per game.
Anyway, I also “chained” the results. What this provides is a timeline adjustment.
For example, in 1948, the timeline adjustment is 0.98. That’s the same timeline adjustment as 1997-98. Therefore, we can say that even though there were about 17,000 per game in 1948, that’s equivalent to the 29,000 per game in 1997-98.
We see that MLB was most popular in 1993-94, though 2007-08 comes close.
Again, I’m providing an honest framework, where the mess of not considering new ballparks should be handled (by someone else).
Generally speaking, the eras can be set as:
1946-1951 (88% attendance of 2009)
1952-1960 (66%)
1961-1976 (58%)
1977-1986 (77%)
1987-2009 (99%)
Data (quasi-formatted… copy to Excel to see it better formatted):
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