2006 True Home Runs

Here is a list of every player in 2006 that hit a Home Run in the Major Leagues, alongside the number of True HRs that player hit. A True Home Run is a HR that, taking atmosphere and wind conditions into account, would have went farther than an average MLB ballpark fence. The raw HR data comes from Hit Tracker, and I calculated the True HR stat. The fourth column gives the percentage of HRs that were True HRs. Enjoy!

Player	HR	True HR	True HR/HR
Albert Pujols	49	46	94%
Ryan Howard	58	46	79%
David Ortiz	54	43	80%
Jermaine Dye	44	39	89%
Frank Thomas	39	37	95%
Andruw Jones	41	37	90%
Travis Hafner	42	36	86%
Alfonso Soriano	46	36	78%
Adam Dunn	40	35	88%
Jim Thome	42	34	81%
Alex Rodriguez	35	33	94%
Aramis Ramirez	38	33	87%
Carlos Beltran	41	32	78%
Matt Holliday	34	31	91%
Lance Berkman	45	31	69%
Troy Glaus	38	31	82%
Vernon Wells	32	30	94%
Adam LaRoche	32	30	94%
Manny Ramirez	35	30	86%
Jason Bay	35	29	83%
Jason Giambi	37	29	78%
Mark Teixeira	33	29	88%
Jeff Francoeur	29	28	97%
Paul Konerko	35	28	80%
Carlos Delgado	38	28	74%
Bill Hall	35	28	80%
Richie Sexson	34	27	79%
Chase Utley	32	27	84%
Torii Hunter	31	26	84%
Garrett Atkins	29	26	90%
Justin Morneau	34	25	74%
Vladimir Guerrero	33	25	76%
Nick Swisher	35	25	71%
Prince Fielder	28	24	86%
Joe Crede	30	24	80%
Marcus Thames	26	23	88%
Carlos Lee	37	23	62%
Ken Griffey Jr.	27	23	85%
Barry Bonds	26	22	85%
Pat Burrell	29	22	76%
Craig Monroe	28	22	79%
Raul Ibanez	33	22	67%
Nick Johnson	23	21	91%
Chipper Jones	26	21	81%
Ray Durham	26	21	81%
Daniel Uggla	27	21	78%
Magglio Ordonez	24	21	88%
Eric Byrnes	26	21	81%
Brandon Inge	27	20	74%
Austin Kearns	24	20	83%
Mike Cuddyer	24	20	83%
Adrian Gonzalez	24	20	83%
Lyle Overbay	22	20	91%
Pedro Feliz	22	20	91%
Miguel Cabrera	26	20	77%
Grady Sizemore	28	19	68%
Juan Rivera	23	19	83%
Chris Duncan	22	19	86%
Jacque Jones	27	19	70%
Mike Piazza	22	19	86%
Brad Hawpe	22	18	82%
Mike Cameron	22	18	82%
Jorge Posada	23	18	78%
Moises Alou	22	18	82%
Phil Nevin	22	18	82%
Brian McCann	24	18	75%
Jimmy Rollins	25	18	72%
Shea Hillenbrand	21	18	86%
David Wright	26	18	69%
Adrian Beltre	25	18	72%
Jonny Gomes	20	17	85%
Eric Chavez	22	17	77%
Juan Encarnacion	19	17	89%
Johnny Damon	24	17	71%
Jim Edmonds	19	17	89%
Josh Willingham	26	16	62%
Nomar Garciaparra	20	16	80%
Scott Rolen	22	16	73%
Aubrey Huff	21	16	76%
Juan Uribe	21	16	76%
Chad Tracy	20	16	80%
Mark Teahen	18	16	89%
Mike Jacobs	20	16	80%
Miguel Olivo	16	16	100%
Chris Shelton	16	15	94%
Curtis Granderson	19	15	79%
Gary Matthews Jr.	19	15	79%
Morgan Ensberg	23	15	65%
Miguel Tejada	24	15	63%
Jose Bautista	16	15	94%
J.D. Drew	20	15	75%
Rich Aurilia	23	15	65%
Ramon Hernandez	23	14	61%
Carl Crawford	18	14	78%
Brad Wilkerson	15	14	93%
Jose Reyes	19	14	74%
Ryan Zimmerman	20	14	70%
Ty Wigginton	24	14	58%
Rafael Furcal	15	14	93%
Geoff Jenkins	17	14	82%
Bengie Molina	19	14	74%
Ben Broussard	21	14	67%
Jose Valentin	18	13	72%
Robinson Cano	15	13	87%
Michael Barrett	16	13	81%
Mark DeRosa	13	13	100%
Eric Hinske	13	13	100%
Jorge Cantu	14	13	93%
Bobby Abreu	15	13	87%
Carlos Guillen	19	13	68%
Milton Bradley	14	13	93%
Craig Wilson	17	13	76%
Orlando Hudson	15	13	87%
Dave Ross	21	13	62%
Edwin Encarnacion	15	13	87%
Shawn Green	15	13	87%
Xavier Nady	17	13	76%
Hank Blalock	16	13	81%
Rocco Baldelli	16	12	75%
Wilson Betemit	18	12	67%
Casey Blake	19	12	63%
Todd Helton	15	12	80%
Damon Hollins	15	12	80%
Conor Jackson	15	12	80%
Ian Kinsler	14	12	86%
Nicholas Markakis	16	12	75%
Kenji Johjima	18	12	67%
Joshua Barfield	13	12	92%
Russell Branyan	18	12	67%
Alexis Rios	17	12	71%
Ivan Rodriguez	13	12	92%
Craig Biggio	21	12	57%
A.J. Pierzynski	16	12	75%
Hanley Ramirez	17	12	71%
Melvin Mora	16	12	75%
Bernie Williams	12	11	92%
Emil Brown	15	11	73%
Brandon Phillips	17	11	65%
Derek Jeter	14	11	79%
Garret Anderson	17	11	65%
Ronnie Belliard	13	11	85%
Edgar Renteria	14	11	79%
Victor Martinez	16	11	69%
Kevin Youkilis	13	11	85%
Matt Stairs	13	11	85%
Marcus Giles	11	11	100%
Tadahito Iguchi	18	11	61%
Kevin Millar	15	11	73%
Matt Murton	13	11	85%
Greg Norton	17	11	65%
Preston Wilson	17	11	65%
Reed Johnson	12	11	92%
Michael Napoli	16	10	63%
Eliezer Alfonzo	12	10	83%
Rod Barajas	11	10	91%
Aaron Rowand	12	10	83%
Joe Borchard	10	10	100%
Luis Gonzalez	15	10	67%
Scott Spiezio	13	10	77%
Kevin Mench	13	10	77%
Jeff Kent	14	10	71%
Marlon Anderson	12	10	83%
Mike Lamb	12	10	83%
Brian Giles	14	10	71%
John Buck	11	10	91%
Gregg Zaun	12	10	83%
Corey Patterson	16	10	63%
Khalil Greene	15	10	67%
Felipe Lopez	11	9	82%
Angel Berroa	9	9	100%
Mike Lowell	20	9	45%
Jay Gibbons	13	9	69%
Jason Varitek	12	9	75%
Carl Everett	11	9	82%
Reggie Sanders	11	9	82%
Cliff Floyd	11	9	82%
Cody Ross	13	9	69%
Wes Helms	10	9	90%
Jose Castillo	14	9	64%
Alex Gonzalez	9	9	100%
Andre Ethier	11	9	82%
Michael Young	14	9	64%
Russell Martin	10	8	80%
Wily Mo Pena	11	8	73%
Jeff Conine	10	8	80%
Jason LaRue	8	8	100%
Mark Ellis	11	8	73%
Robb Quinlan	9	8	89%
David DeJesus	8	8	100%
Josh Bard	9	8	89%
Todd Walker	9	8	89%
Jeromy Burnitz	16	8	50%
Randy Winn	11	8	73%
Matthew Kemp	7	7	100%
Carlos Quentin	9	7	78%
Jason Lane	15	7	47%
Olmedo Saenz	11	7	64%
Mark Bellhorn	8	7	88%
Rondell White	7	7	100%
Ryan Church	10	7	70%
Corey Koskie	12	7	58%
Bobby Crosby	9	7	78%
Nate McLouth	7	7	100%
Tim Salmon	9	7	78%
Luke Scott	10	7	70%
Javier Valentin	8	7	88%
Corey Hart	9	7	78%
Derrek Lee	8	7	88%
Travis Lee	11	7	64%
Damion Easley	9	7	78%
Joe Mauer	13	7	54%
Jose Lopez	10	7	70%
Julio Lugo	12	7	58%
Jack Wilson	8	7	88%
Jay Payton	10	7	70%
Orlando Cabrera	9	7	78%
Yuniesky Betancourt	8	7	88%
Scott Hatteberg	13	7	54%
David Bell	10	7	70%
Jhonny Peralta	13	7	54%
Gerald Laird	7	6	86%
Hideki Matsui	8	6	75%
Dmitri Young	7	6	86%
Bobby Kielty	8	6	75%
Ronny Cedeno	6	6	100%
Aaron Guiel	7	6	86%
David Dellucci	13	6	46%
Daryle Ward	7	6	86%
Mike Lieberthal	9	6	67%
Jose Guillen	9	6	67%
Brian Roberts	10	6	60%
Sean Casey	8	6	75%
Javy Lopez	8	6	75%
Ryan Shealy	7	6	86%
Yorvit Torrealba	7	6	86%
Johnny Estrada	11	6	55%
Eli Marrero	6	6	100%
Chris Coste	7	6	86%
Hector Luna	6	6	100%
Ryan Langerhans	7	6	86%
Eduardo Perez	9	5	56%
Clint Barmes	7	5	71%
Dan Johnson	9	5	56%
Mark Kotsay	7	5	71%
Toby Hall	8	5	63%
Dioner Navarro	6	5	83%
Jeff DaVanon	5	5	100%
Jose Cruz	5	5	100%
Henry Blanco	6	5	83%
Andy Phillips	7	5	71%
Jason Kubel	8	5	63%
Tony Clark	6	5	83%
Dallas McPherson	7	5	71%
Chris Snyder	6	5	83%
Mike Sweeney	8	5	63%
Jose Vidro	7	5	71%
Jeremy Hermida	5	5	100%
Gary Sheffield	6	5	83%
Steve Finley	6	5	83%
Todd Hollandsworth	7	5	71%
Ryan Doumit	6	5	83%
Tony Graffanino	7	5	71%
Aaron Boone	7	5	71%
Mike Rivera	6	5	83%
Rickie Weeks	8	5	63%
Eric Munson	5	5	100%
Coco Crisp	8	5	63%
Jamey Carroll	5	5	100%
Matt Diaz	7	5	71%
Jeff Baker	5	5	100%
Brian Anderson	8	4	50%
Trot Nixon	8	4	50%
Ramon Castro	4	4	100%
Yadier Molina	6	4	67%
John Mabry	5	4	80%
Aaron Hill	6	4	67%
Mark Grudzielanek	7	4	57%
Scott Thorman	5	4	80%
Lew Ford	4	4	100%
Endy Chavez	4	4	100%
Stephen Drew	5	4	80%
Shane Victorino	6	4	67%
Chris Burke	9	4	44%
Ichiro Suzuki	9	4	44%
Nelson Cruz	6	4	67%
Omar Vizquel	4	4	100%
Freddie Bynum	4	4	100%
Gabe Gross	9	4	44%
Ryan Freel	8	4	50%
Damian Miller	6	4	67%
Angel Pagan	5	4	80%
Alex Escobar	4	4	100%
Alex Cintron	5	4	80%
Vinny Castilla	5	4	80%
Doug Mirabelli	6	4	67%
Adam Everett	6	4	67%
Freddy Sanchez	6	4	67%
Jason Michaels	9	4	44%
Jason Smith	5	4	80%
Reginald Abercrombie	5	4	80%
Chone Figgins	9	4	44%
Lastings Milledge	4	4	100%
Joe Randa	4	3	75%
Shin-Soo Choo	3	3	100%
Tony Batista	5	3	60%
Todd Pratt	4	3	75%
Willy Aybar	4	3	75%
Ryan Garko	7	3	43%
Gary Bennett	4	3	75%
Jose Hernandez	3	3	100%
Sal Fasano	5	3	60%
Vance Wilson	5	3	60%
Ryan Spilborghs	4	3	75%
Andy Marte	5	3	60%
Lance Niekro	5	3	60%
Mark Sweeney	5	3	60%
Rob Mackowiak	5	3	60%
Melky Cabrera	7	3	43%
Jeff Cirillo	3	3	100%
Adam Kennedy	4	3	75%
Paul Lo Duca	5	3	60%
Doug Mientkiewicz	4	3	75%
Craig Counsell	4	3	75%
Frank Catalanotto	7	3	43%
Alfredo Amezaga	3	3	100%
Juan Pierre	3	3	100%
Juan Castro	3	3	100%
Jeremy Reed	6	3	50%
J.J. Hardy	5	3	60%
Omar Infante	4	3	75%
Jose Molina	4	3	75%
James Loney	4	3	75%
Ben Johnson	4	3	75%
Jorge Piedra	3	3	100%
Russ Adams	3	3	100%
Ronny Paulino	6	3	50%
Ricky Ledee	2	2	100%
Terrmel Sledge	2	2	100%
Adam Lind	2	2	100%
Adam Melhuse	4	2	50%
Kevin Kouzmanoff	3	2	67%
Dustin Pedroia	2	2	100%
Christopher Young	2	2	100%
Delmon Young	3	2	67%
Damian Jackson	4	2	50%
Bill Mueller	3	2	67%
JD Closser	2	2	100%
Luis Matos	2	2	100%
Chris Snelling	3	2	67%
Rob Bowen	3	2	67%
Eric Young	3	2	67%
Kevin Frandsen	2	2	100%
Brian Jordan	3	2	67%
Jeff Mathis	2	2	100%
Ross Gload	3	2	67%
Cory Sullivan	2	2	100%
Joshua Rabe	3	2	67%
Chris Woodward	3	2	67%
Shane Costa	3	2	67%
Kazuo Matsui	3	2	67%
John McDonald	3	2	67%
Esteban German	3	2	67%
Howard Kendrick	4	2	50%
Kenny Lofton	3	2	67%
Placido Polanco	4	2	50%
Chris Duffy	2	2	100%
Abraham Nunez	2	2	100%
Royce Clayton	2	2	100%
Aaron Miles	2	2	100%
Brady Clark	4	2	50%
Maicer Izturis	5	2	40%
Brian Schneider	4	2	50%
Joe Inglett	2	2	100%
Marlon Byrd	5	2	40%
Matt Treanor	2	2	100%
Choo Freeman	2	2	100%
Shannon Stewart	2	2	100%
Mark Loretta	5	2	40%
John Rodriguez	2	2	100%
Ramon Martinez	2	2	100%
Mike Matheny	3	2	67%
Todd Greene	2	2	100%
Julio Franco	2	2	100%
Kendry Morales	5	2	40%
Kevin Witt	2	1	50%
Rene Rivera	2	1	50%
Marco Scutaro	5	1	20%
So Taguchi	2	1	50%
Jose Vizcaino	2	1	50%
Miguel Ojeda	2	1	50%
Carlos Joaquin Ruiz	3	1	33%
Christopher Iannetta	2	1	50%
Luis A. Gonzalez	2	1	50%
Jeff Keppinger	2	1	50%
Matt LeCroy	2	1	50%
Brad Ausmus	2	1	50%
Nick Green	2	1	50%
Brandon Fahey	2	1	50%
Scott Podsednik	3	1	33%
Ryan Theriot	3	1	33%
Tomas Perez	2	1	50%
Chris Gomez	2	1	50%
Luis Castillo	3	1	33%
David Newhan	4	1	25%
Jason Repko	3	1	33%
Gabe Kapler	2	1	50%
Pablo Ozuna	2	1	50%
Luis Rodriguez	2	1	50%
Geoff Blum	4	1	25%
David Eckstein	2	1	50%
Benjamin Zobrist	2	1	50%
Kelly Shoppach	3	1	33%
Jason Ellison	2	0	0%
Todd Linden	2	0	0%
Robert Fick	2	0	0%
Jason Bartlett	2	0	0%
Fernando Tatis	2	0	0%
Neifi Perez	2	0	0%
Dave Roberts	2	0	0%
Chad Moeller	2	0	0%

Explanation of Sabermetrics - Power Hitting

(NOTE: I realize a small error in my queries, so some of the numbers I had posted were incorrect. I fixed the error and corrected the numbers, but there was little difference in the results.)

Ask almost any sabermetrician what the most difficult facet of baseball to predict is. You will hear one answer from almost every single one: season to season pitcher performance. While I will certainly agree that this is a very difficult thing to predict, I have found trouble in predicting something else, something from the other side of home plate: power hitting.

While contact hitting remains fairly constant over the years, a hitter's ability to hit for power is always fluctuating. Younger guys are developing power, older guys are losing power. Contact hitting is much more basic. Either you are a good contact hitter or you are not. Sure, you can change your swing or learn more about it as you mature, but overall the ability to hit for contact is fairly linear. A poor contact hitter one year isn't going to become a good one the next. But a player who is a poor power hitter can become a good one the next year, which is what makes power hitting such an interesting - and difficult to study - topic.

So how do you predict power, and why is it so difficult? The most basic way is to use Doubles, Triples, or a combination of the two. The thinking behind this is that as a player matures and gets stronger, these Extra Base Hits will have enough power behind them to clear the fence instead of staying in the yard for a multi-bagger. I tested this theory among players with at least 200 At-Bats in 2004, 2005, and 2006. 73 of these players increase their AB/2B from 2004 to 2005 by more than 1.5 points. From 2005 to 2006, just 27 of these players increased their AB/HR. That’s just 37%. If 2Bs are supposed to predict power, why were they only successful 37% of the time?

Doubles and Triples, I believe, are not the best source for power prediction. Approximately 14% of all doubles are groundballs, and as we know groundballs can never be converted into a home run. Doubles are especially tricky when looking at hitters who have some speed. They may hit a line drive or a bloop into the shallow part of the outfield that would be a single for slow batters, but because of their speed are able to turn it into a double. This is nice to be able to do, but it has nothing to do with potential future home run power. Or, these same players may actually hit a ball well, but because of their speed turn their double into a triple.

This begs two questions. One, what if we exclude fast players from our sample? I took out any player who had 20 or more steals in any of the 3 years, and the percentage actually decreased to 35%.

The second question is why don’t we include triples? Well, because triples are even more flawed than doubles are. Triples are almost the sole property of fast players. Slower players will very rarely hit a triple and will almost never hit more several in a season. Again though, why not include them? They just seem to be an extension of doubles for fast players. The problem is that 42% of triples are groundballs, usually hit down the first base line and into the corner in right field so that the fast runner has a chance to make it to third. This has nothing to do with the type of power we are talking about.

If we do include 3Bs and exclude fast players, we get 59 players increasing their AB/(2B+3B) from 2004 to 2005 and 19 increasing their AB/HR from 2005 to 2006. That's about 32%.

If the flaws with doubles and triples aren't enough to persuade you, there is a whole other category of hits that prove troublesome that you won't be able to find in any box score or stat sheet. Thus far I have ignored the most important condition of a double... that it is a ball in play. Balls in play are never to be entirely trusted because once they are put in play, they are out of the control of the pitcher and the batter and put into the hands of luck and defense, a sabermetrician's biggest adversary. There are plenty of well-hit balls that do not fall for hits because of bad luck or amazing defense. On the flip-side of the coin, there are plenty of balls that fall for hits that don't deserve to because of that slow, injury-ridden left fielder that happened to be playing at the time the ball was put into play. Essentially, because doubles and triples are balls in play, they will never be capable of being fully trusted.

So basically, a plethora of stats seem to be either flawed or meaningless that were otherwise thought to be useful… some even considered to be "sabermetric" in nature. Here is a list of a few common ones:

• Doubles
• Triples
• Total Bases
• Slugging Percentage
• Isolated Power
• On-Base Plus Slugging Percentage (OPS)
• Secondary Average

Now I realize I have done a good job bashing doubles and triples, but the fact remains: they are our only guide to go on for predicting power. So how should we go about prediciting power if 2Bs and 3Bs aren’t very accurate? The answer to that, right now, is that we can't - at least with the primitive stats that we have to work with. With access to batted ball data for every player, we would be able to set parameters for what is considered a well-hit ball, add up all of these for each player, and defining power could conceivably be quite easy from there. This seems to be the only sure-fire way to see who the players who have a true chance of developing - or losing - power are.

Hit Tracker is a service that seems to have realized exactly what I am talking about. Last year they tracked every home run hit by every player and, using physical science, determined each Home Run’s “True Distance,” in addition to a number of other bits of information about the hit. We use this data to determine our “True Home Run” stat. We take each home run’s true distance and the location where it went over the fence and place it into a theoretical, Average MLB Ballpark. From there, we can determine how many would have actually gone out in this ballpark and which wouldn’t have.

The problem with this is that only Home Runs are included by Hit Tracker. To get a true sense of a hitter’s power, we’d need this data on all airballs, not just the ones to clear the fence. What about the ones that should have but didn’t? Hit Tracker will be correcting this in 2007 by tracking all airballs, but unless they do this for past years, it may be a while before we can develop a very accurate power prediction system.