Taking a Look at Changes in Contact Rate

Back in 2009, Eric Seidman wrote a piece here that looked into when samples become reliable for certain statistics. The piece was based off work done by Pizza Cutter. You can read the piece here for a full explanation of how the conclusions were reached, but below is a list showing how many PAs it takes for a statistic to become reliable.

50 PA: Swing %
100 PA: Contact Rate
150 PA: Strikeout Rate, Line Drive Rate, Pitches/PA
200 PA: Walk Rate, Groundball Rate, GB/FB
250 PA: Flyball Rate
300 PA: Home Run Rate, HR/FB
500 PA: OBP, SLG, OPS, 1B Rate, Popup Rate
550 PA: ISO

Prior to yesterday’s games, the cut off for qualified hitters in 2013 was 99 PA. As a result, we can now look at 2013 Contact% and compare it to career rates to see who is making contact at a much higher or much lower rate so far this season.

The average gap in 2013 Contact% and career Contact% was -0.22%. The standard deviation was 3.41%. No players were two standard deviations or more above the mean, but six were two standard deviations or more below the mean. They are listed below.

Name

2013 Contact%

Career Contact%

Contact% Gap

Pedro Alvarez

62.50%

69.90%

-7.40%

Albert Pujols

78.30%

85.80%

-7.50%

Dan Uggla

64.30%

72.70%

-8.40%

Jeff Francoeur

69.40%

77.80%

-8.40%

Jason Castro

73.50%

82.20%

-8.70%

Colby Rasmus

59.20%

75.90%

-16.70%

 

This is kind of a big deal because there is a strong correlation between Contact% and K%. I did a quick regression test using the 179 hitters who had 100+ PA last year, and got an r-squared of 0.8192. Not that the relationship of those two things wasn’t obvious beforehand.

contact regression

Aside from Pujols, all of these guys were high strikeout hitters to begin with. But this is further evidence that Pujols is in full on decline mode. When I calculated average and standard deviation for the gap between 2013 Swing% and career Swing%, Pujols was one of only four players whose gap in Swing% is more than two standard deviations above the mean. In other words, not only is Pujols making 7.5% less contact, he’s also swinging at 5.8% more pitches. Not a good combination.

There were two other notable names that showed up fairly high on the “making less contact” list, slow starters B.J. Upton and Jay Bruce. Their contact percentages are down 6.5% and 4.1%, respectively. Obviously, their strikeout rates are way up, and their slash lines don’t look like they have in the past.

Of the two, you should be less worried about B.J. He’s walking more than he did last year, and his BABIP is a miniscule .209.

On the other hand, Bruce should be a serious concern. His average is right around .250 like it always is, but he has needed a .362 BABIP to keep it there. When the luck goes away and he’s left with significantly worse contact skills, the average may fall off the map. It would be tough to sell high since the power hasn’t been there and because he hasn’t even been a top 70 outfielder according to ESPN’s player rater. But if there is someone out there hoping for a rebound and still willing to pay 75 cents on the dollar, take it.

At the top of the “making more contact” list, you unsurprisingly find the names of some of the biggest surprises of the season. Nate McLouth and Matt Carpenter have the largest positive gaps between their 2013 and career contact rates at 6.3% and 6%, respectively. McLouth’s plate discipline numbers are super impressive as he has a 14% BB% and a 9.1% K%. Thanks to the increase in contact, McLouth is currently a top five outfielder per the player rater, and Carpenter has been a top ten option at both second and third base.

Both guys are also buy-high candidates. Neither one is relying on a completely unsustainable BABIP. They’re both a little above average at .315 and .316, but they aren’t going to get hit too hard by regression. If someone added them off the wire and is looking to cash in and sell high before they regress, take them up on that offer.

Mark Reynolds also shows up in 8th on the list with a 4.7% increase in Contact%. As a result, his K% is 7.3% lower than his career average. That has led to a .280 batting average (.290 BABIP) for Mark freaking Reynolds. You can’t predict ball.

Below is a list of those with a gap between their 2013 and career contact rates that is more than one standard deviation above the mean.

Name

2013 Contact%

Career Contact%

Contact% Gap

Nate McLouth

92.00%

85.70%

6.30%

Matt Carpenter

91.40%

85.40%

6.00%

Torii Hunter

82.60%

76.80%

5.80%

Chris Davis

74.40%

68.80%

5.60%

Trevor Plouffe

84.40%

79.10%

5.30%

Alfonso Soriano

79.30%

74.10%

5.20%

Mark Reynolds

68.90%

64.20%

4.70%

Lorenzo Cain

84.40%

79.70%

4.70%

Josh Rutledge

84.20%

79.50%

4.70%

Donovan Solano

89.30%

84.70%

4.60%

Jonathan Lucroy

90.40%

85.80%

4.60%

Angel Pagan

92.50%

87.90%

4.60%

James Loney

92.40%

87.90%

4.50%

Jed Lowrie

88.60%

84.20%

4.40%

Josh Donaldson

82.40%

78.00%

4.40%

Joey Votto

82.50%

78.20%

4.30%

Starling Marte

79.60%

75.40%

4.20%

Adrian Beltre

84.30%

80.10%

4.20%

Manny Machado

83.10%

78.90%

4.20%

Ben Zobrist

87.70%

83.70%

4.00%

Russell Martin

86.30%

82.30%

4.00%

Shin-Soo Choo

80.20%

76.20%

4.00%

Jayson Werth

82.10%

78.10%

4.00%

Matt Holliday

82.40%

78.40%

4.00%

Ruben Tejada

89.70%

85.90%

3.80%

Miguel Cabrera

82.80%

79.20%

3.60%

Greg Dobbs

85.50%

82.00%

3.50%

Austin Jackson

82.90%

79.40%

3.50%

Norichika Aoki

92.40%

89.00%

3.40%

John Buck

78.20%

74.80%

3.40%

Justin Morneau

84.00%

80.80%

3.20%



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You can find more of Brett's work on TheFantasyFix.com or follow him on Twitter @TheRealTAL.


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Eric
Guest
Eric

What does it mean that a statistic has “become reliable”? Does it mean we should expect the contact rate of these hitters to remain about where it is for the rest of the season, or for those whose contact rate is higher than career avg. we should expect it to come back down, vice versa?

Frank
Guest
Frank

That you’d expect the stat for the rest of the season to be closer to what it is now than what pre-season projections said.

Skin Blues
Member
Member
Skin Blues

Here is how Tango suggests the data be used:

“I like to get things to r=.50. You’ll see the reason in a minute. If r=.70, when PA=300, then r=.50 when PA=130. It’s not important how I got that for now.

Ok, so what can you do with that?

This means that you can add 130 PA of league average HR/FB to any player, to get an estimate of his true talent.”

And to get to r=.5 he suggests multiplying the r=.7 value by 3/7. So it’s a bit of math and research to look up league averages. Pizza Cutter describes it as this: “At .70, the rate of signal to noise crosses the halfway point”. Tango’s method seems more accurate, and Pizza’s method seems quicker. Seems they’ve been arguing over which one to use for about a decade, now.

Fischer
Member
Fischer

Razzball’s Jaywrong wrote about this recently. If you’re looking for more analysis and explanation about what these PA benchmarks mean, I suggest checking it out.

http://razzball.com/when-is-a-streak-not-a-streak-anymore/

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