The Hardball Times -- Greg Rybarczyk

The 2009 High School Power Showcase

Greg Rybarczyk — 2009-01-23T05:01:15+00:00

As we all know, Jan. 1, 2009 was a day that had no parallel in the world of sports. I am of course referring to the debut of the MLB Network, a 24/7 channel devoted entirely to baseball. (Orange Bowl? Isn’t that what they have first-year art students draw pictures of?) The MLB Network, along with Extra Innings and DirecTV’s Strike Zone Channel, is just one more thing they can take away from me only after they pry the remote control out of my cold, dead hands…

Nevertheless, I spent Jan. 1 on a plane, headed for St. Petersburg, Fla., because there I had the opportunity to witness the top 69 high school hitters in the United States and nine other countries swinging for the fences in the Third Annual International High School Power Showcase Home Run Derby, held for the second straight year at Tropicana Field. I was invited to provide distance measurements for the home runs hit during the event, using Hit Tracker, and I came away mightily impressed with the talent assembled under the dome in St. Pete.

The Showcase is the creation of Brian Domenico, a 35-year-old former professional baseball player now coaching high school ball in Boca Raton, Fla. Domenico organized the first Power Showcase in 2004 as a South Florida event held at Olympic Heights High School in Boca Raton. It was a great success, and the following year the showcase became a statewide Florida tournament, then a regional tournament, then national in December 2006. For the last two years, the Power Showcase has included representatives from the U.S. and several foreign nations.

Showcase Day 1:

On Friday, Jan. 2, the participants took batting practice and executed running and fielding drills under the instruction of members of the New York Yankees spring training staff at their baseball complex in Tampa. Under sunny skies, and backed by a crisp breeze, a couple of players generated a traffic hazard on nearby Dale Mabry Highway with some long homers. Later, several hitters threatened to make me regret declining the extra insurance on my rental car, which I unwisely parked beyond the left field fence of one of the four practice fields.

That evening, the Showcase participants gathered in the Grand Banquet Ballroom of the Bayfront Hilton in St. Petersburg for a formal dinner and the presentation of their uniforms. The dinner began with opening remarks from Showcase President Domenico, who presented a plaque to Van Conway, of Conway MacKenzie and Dunleavy, in appreciation for that firm’s sponsorship of the international Showcase players.

Next came Davey Johnson, 2008 U.S. Olympic and 2009 U.S. World Baseball Classic managerDavey Johnson Johnson shared stories from his long baseball career, in which he won two World Series as a player and one more as a manager. His most memorable recollection came from Game 2 of the 1966 Series, in which, at 23, he got a hit off Sandy Koufax. Koufax retired after that season, and Johnson’s hit turned out to be the last he allowed in his career. The next year at spring training, Johnson approached Koufax and introduced himself, mentioning the hit from the previous season. Koufax’s response: “Why do you think I retired?”

Perry Husband from Hitting is a Guess provided a demonstration of its system for video-based analysis of pitch recognition and swing mechanics. Video overlays showed the difference in release points for different pitch types, highlighting the potential value of disguising pitches, or of seeing through the disguise. High-speed video of swings, combined with geometric tools for measuring lines and angles, provided a tool for identifying and optimizing swing characteristics to improve exit speed, or how hard the ball is hit.

Linda Ruth Tosetti, granddaughter of Babe Ruth, was the final speaker. She offered her encouragement to the hitters, and described some of her famous grandfather’s ties to the St. Petersburg region. Notable was a long home run Ruth hit during spring training one year that was reported to have traveled more than 600 feet. I can’t offer any confirmation of the distance on Ruth’s homer, but I can attest that when you read about the home runs hit in the Power Showcase, the numbers you read are “how far they really went”!

Day 2: The Home Run Derby

Beginning just before 9 a.m. on Saturday, each of the 69 hitters took his swings in the preliminary round. In groups of three, each player batted off a pitching machine, first with a wooden bat for 10 outs, and after the others in the group had batted, each player batted for 15 outs with a metal bat. Any swing not resulting in a home run counted as an out. After all hitters had taken their turn, the five with the highest number of home runs passed through to the final round for an additional 15 outs with metal bats. It was a long day: Aside from occasional two-minute breaks to replenish baseballs, and a 10-minute break before the finals, the action did not stop for more than 12 hours!

Preston Overbey from Jackson, Tenn., drew the unenviable leadoff slot, and after coming up empty with the wood, he ripped the first of 271 total home runs in the showcase, a line drive to left-center field that narrowly cleared the fence. Overbey totaled three homers to set the early pace, and was matched in the lead by Tyler Garrone (Pennsylvania) and Adam Walker II (Wisconsin), before Ryan Gunhouse (Texas) broke out with 10 home runs, including two with his wooden bat. Matthew Kirkland (Tennessee) managed only three homers in all, but he hit the first of 16 homers to reach the upper deck in left field, and the first of 21 balls to hit a catwalk during the event.

Brett Sanders, representing Canada, racked up seven home runs to jump into second place, including a 408-foot home run with his wooden bat (tied for the longest wood bat homer of the event), and a 478-foot blast with his metal bat to deep left-center field that would stand up as the longest of the event for more than eight hours, although not all the way to the end. Jacob Mayers (Virginia) knocked three balls over the fence with his wooden bat, and a total of seven, to move into a tie for second place. Christian Walker (Pennsylvania) followed with three wood bat homers, and seven more with the metal bat, to move into a tie for the lead; his 10 home runs included a 468-footer to the upper level of the Power Alley Pub in deep center field.

Miles Head (Georgia) knocked nine homers to gain a place in the top five. Tommy Joseph (Arizona) crushed three home runs in his metal bat round that traveled more than 450 feet, including two to the upper deck and another 465-foot drive that hit the U.S. flag hanging in deep left-center field some 75 feet above field level. Dante Bichette (Florida), son of the eponymous former major leaguer, showed off his sweet swing by hitting two wood bat homers and nine more with the metal for a total of 11, to take the lead. Corey Davis (Georgia) got off to a slow start, but in the second half of his metal bat round, he ripped five long homers, including shots of 468, 464 and 455 feet. Jayce Boyd (Florida) tallied nine home runs, and Matt Conway (Michigan) smashed eight that averaged 435 feet.

After several more hitters took their turns, Randal Grichuk (Texas) forged into the lead with 12 homers, including two catwalk shots and four balls that reached the upper deck. His longest ball came off the bat at just over 122 mph, covered 477 feet and hit the “Fast news you can use” sign above the Party Deck just inside the left field foul pole. Cody Geyer (North Carolina) and Chris Marconcini (Tennessee) each totaled seven homers, and Josh Leyland (California) launched a 472-foot rocket onto the roof of the restaurant in center field, but no other hitters were able to crack the top five until Nevada’s Bryce Harper took his turn.

Like a lot of hitters in the Showcase, Harper wasn’t able to hit any home runs with his wooden bat, but he showed a beautiful left-handed swing and hit several balls hard. My Hit Tracker assistant Brenton Blair and I agreed that he was a good candidate to get some out with the metal bat, but nevertheless we weren’t ready for what we were about to witness. Stepping back to the plate with his metal bat, Harper knocked his first homer 443 feet on a line to the back of the right field seats. Three unremarkable homers and a number of outs followed, but then over the next 60 seconds, Harper unleashed an awe-inspiring series of hits to areas of Tropicana Field few major leaguers have reached:

• 460 feet to the top edge of the Jumbotron in right field; 119 mph off the bat
• 484 feet to the back wall of the stadium, 15 feet above the Jumbotron; 122 mph
• 485 feet to the back wall, just below the orange Bright House “target” sign; 123 mph
• 405 feet on a blistering line drive around the RF pole; 118 mph
• 502 feet to the back wall, in the vicinity of the first “A” in the Tropicana Field sign, 20 feet above the top of the Jumbotron; 124.5 mph
• 477 feet to right-center field, halfway up and a few feet to the left of the Jumbotron; 119 mph.

That’s six home runs, averaging 469 feet and 121 mph off the bat—all struck by a 16-year-old high school sophomore. Here's a picture of where each home run fell:

Photo by Jeff Horton

Harper hit 11 homers in all, enough to make the top five and join Gunhouse, Walker, Bichette and Grichuk in the finals. Walker led off with nine homers, and Gunhouse followed with eight. Harper, third, was able to hit only one more ball out. He looked worn out, understandably so since he had the misfortune to have hit 67th out of 69 batters, and had only a few minutes to recover before the finals. Grichuk hit fourth and scored eight homers including a 454-foot bomb, longest of the finals. Bichette hit last and scored four long balls, but when his last out landed on the warning track just short of the 370 sign in left field, the clock read 9:17 p.m. and Christian Walker had become the 2009 Power Showcase champion.

Wood vs. metal bats

The format of the Showcase provided an excellent opportunity to quantify the difference between power hitting with metal bats and wooden bats, since each hitter got 10 outs with wood and 15 with metal. The fact that each player started with wood and then switched to metal is not ideal, from a scientific standpoint, because the data could be biased due to the players being more warmed up for the metal round, or more fatigued in the metal round. However, because the players batted in groups of three, with time between their wood and metal rounds, both of these potential biases should be reduced.

The most obvious comparison method is average distance with wood vs. average distance with metal. The 30 wood bat homers averaged 373.1 feet, while the 230 metal bat homers that were measured averaged 408.1 feet (a few catwalk homers have not yet been analyzed). That’s a difference of 35 feet, or about 9.4 percent. A similar comparison of speed off the bat yields 100.6 mph for wood, and 108.9 mph for metal bats, a difference of 8.3 mph, or 8.3 percent.

The small sample size for wood bat homers means that there remains a lot of uncertainty in the “translation factor” from wood to metal bats, but knocking roughly 10 percent off the distance of a home run hit by a high school or college slugger can provide a good rough estimate of how far it might have gone with a wooden bat. For line drives that don’t clear the fence, knocking off about 8 percent of the speed off bat will give a reasonable wooden bat estimate.

Next year:

The International Power Showcase was a resounding success in 2009, and we certainly can look forward to watching another collection of outstanding high school sluggers swing for the fences in 2010. If Domenico has his way, perhaps next year’s event might be rotated to another warm-weather ballpark such as Chase Field or Minute Maid Park, and perhaps baseball fans will be able to watch it on television. Either way, your best chance to see the future sluggers of the major leagues “before they were famous” will be in the 2010 International Power Showcase.

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Seeing is believing

Greg Rybarczyk — 2008-02-28T04:50:15+00:00

Some baseball writers have suggested that some of the more, shall we say, devoted members of the sabermetric community should leave their mother’s basement and go watch a game. I disagree; instead of merely watching a game, they should observe a game. Because I believe that the future of sabermetrics lies in Observational Analysis.

Observational Analysis involves numbers, but it is not about statistics in the traditional sense. It is about creating a complete and permanent record of what happens on the field, so that in-game events can be reviewed and analyzed at any time in the future.

Most times, analysts have only traditional stats to work with, spiced with imperfect anecdotal recollections of the game and its plays. This gap makes it very hard to figure out why the results that happened, happened—and the latter, of course, is the key to figuring out how likely they are to happen again.

Observational Analysis is about scrutinizing, and in some cases measuring or timing, important elements of the game, in order to decipher the internal workings of the game of baseball in unprecedented depth and detail.

Current examples of Observational Analysis

One of the greatest innovations for baseball analysis in recent years is Sportvision’s PitchFx, which has opened up many new avenues of inquiry among analysts. PitchFx focuses multiple cameras at the area between the mound and home plate in order to capture the precise trajectory of each pitch. When the data are extracted and examined, we have a perfect example of Observational Analysis, where analysts are manipulating not discrete, box-score counting stats such as plate appearances and hits, but more fundamental parameters like pitch velocity, location and movement.

Observational Analysis of PitchFx data is being published on a nearly daily basis, here at THT and elsewhere, by numerous talented writers, including Joe P. Sheehan, Mike Fast, Josh Kalk, John Walsh and many others.

Furthermore, Sportvision has indicated that we can expect a system (which I will refer to as “HitFx”) that provides the initial trajectory of the ball off the bat. Trajectory data for batted balls is one of the most promising things that Observational Analysis will eventually deliver.

One example of how this is the case was unveiled in my 2008 Hardball Times Annual article, “Of Home Runs and Free Agents.” In the article, I detailed the relationship between Speed Off Bat (SOB—i.e., how hard a player hits the ball) and the outcome of the hit, as measured by slugging average (see the plot below, which shows 2007 data for Andruw Jones and Torii Hunter). Knowing which players hit the ball the hardest (both overall and vs. various factors), or which pitchers (if any) suppress SOB the most, would obviously be very valuable information.

There are several ways to obtain Speed Off Bat data, including a radar gun, an aerodynamic model such as my Hit Tracker, or a camera-based system such as HitFx. Each method has its advantages and drawbacks:

Radar guns are already available in every park, but they require a live operator, and they introduce error based on exactly when the gun is triggered.
Hit Tracker is video-based and thus can be used on any game at any level, and even games from the past. But instead of direct measurement of SOB, it employs an aerodynamic model, and it requires a lot of time.
HitFx will be automatic, and it will measure SOB directly. But it needs to overcome technical hurdles before it even can be said to exist.

Regardless of the method used to obtain this valuable information, there is no alternative to observing the striking and propelling of the ball, which makes such activity a key part of Observational Analysis.

Limitations of Existing Systems

PitchFx is a great step forward, and HitFx most likely will be as well; however, they will not capture all of the important events that take place during a game. For example, unless HitFx were to cover the full trajectory of each hit, it would do nothing to advance the field of defensive analysis.

Here’s why: The landing point of a fly ball can vary enormously due to the effects of wind, temperature and altitude, so by itself, HitFx will never be able to predict the landing point of a fly ball with any greater precision than we get today with the conceptual “defensive zones.” Similarly, knowing the initial trajectory of a grounder off the bat will not allow us to know when and where the ball intersects with the infielders; there are too many variables in all those inelastic ball-ground collisions (also known as "bounces").

Whether we are considering a fly ball or grounder, if we don’t know where it goes with increased precision, we can’t do anything new on defensive analysis. So we need better data. Fortunately, it is within our power to truly revolutionize defensive analysis, using only our eyes and a stopwatch. What follows is an example of how Observational Analysis can reveal the true reasons why certain outcomes transpire.

Example of Observational Analysis: batted ball comparison

On Aug. 25, 2007, in the 4th inning of a game against the St. Louis Cardinals, Atlanta’s Andruw Jones hit a leadoff single. On Sept. 20, 2007, in the 6th inning of a game against the Milwaukee Brewers, Andruw Jones grounded out, 4-3, to end the inning. At a glance, these two events don’t seem to have much in common besides the batter, but beneath the surface there is an interesting story.

A review of the Retrosheet box scores for the two games indicates that the first event was a “single to CF (ground ball)”, while the second event was a “groundout, 2B-1B.” Unfortunately, Retrosheet does not have zone data for either hit, so we can’t tell the direction of the grounders using this source. All we get are the outcomes: For the single, we can assume it went through the middle between Cardinals David Eckstein and Aaron Miles, while for the groundout, we know only that Milwaukee’s Rickie Weeks fielded it and threw out Jones at first.

The best way to dig deeper on these two hits is to actually watch the plays by viewing them on MLB.com (subscription required). For the Aug. 25 single, you can find the single at time 1:41:44 of the 700K stream, or 1:43:53 of the 400K stream. For the Sept. 20 groundout, you can find the video at 2:49:44 of the 700K stream, or 2:48:35 of the 400K stream. I strongly encourage you to go watch the two hits, for if you do, you will realize that, in terms of direction and speed, the balls were struck virtually identically.

Although the two balls skirted the mound and then second base to reach the same spot in the infield at the same time, the outcomes of the two hits were different because of the positioning of the infielders. In the Aug. 25 game, the Cardinals played Andruw Jones to pull, but only mildly so, with shortstop Eckstein towards the hole and second baseman Miles shaded up the middle. Jones’ grounder split the two fielders evenly and reached the outfield grass ahead of Eckstein’s belated dive.

In the Sept. 20 game, the Brewers applied a much stronger shift against Jones, precisely in accordance with the recommendation that I would make two months later in my THT Annual Article. In their strong shift, second baseman Weeks was positioned a couple steps to the shortstop side of second base, where he had only to bend over and drop his glove to scoop Jones’ medium-speed roller and throw him out.

It is interesting to consider how the two hits appear with different types or amounts of information. If you had only the Retrosheet box score, you would never recognize any relationship between the two hits, much less the crucial difference made by defensive positioning. If you also had zone data, you would learn that, on the groundout, Rickie Weeks fielded the ball on the other side of second base. This would be recorded as an “Out Of Zone” play, and, in the absence of any other information, analysts would ever after regard this as a great play by Weeks (though in fact it was as easy as a play can be).

Only via Observational Analysis, tracking each hit and noting the initial positions of the fielders, would you really understand what happened and why. You would recognize that the two hits were identical, and that the play outcomes were different only because of the initial positioning of the infielders. The decision to employ a strong shift made the difference, and it reflects great credit on the Milwaukee organization for acquiring and acting on the information that led them to make that decision.

Observational Analysis: Something we can all get behind

Interestingly, Observational Analysis is an approach that should appeal to both kinds of baseball enthusiasts. You know who I’m referring to: the Stats Guys who argue with numbers, and the Non-stats Guys who argue with words. Neither group has been very effective at converting the other to its way of thinking; their methods of persuasion reflect their own outlook instead of the others’, thus they mostly talk past each other (particularly when it comes to Hall of Fame voting)!

Although some Stats Guys may cling to the idea that all the answers can be found in a box score, most will think that they’ve died and gone to heaven when they dig into a database that has the precise trajectory of every pitch and hit for an entire season, along with the locations of all the fielders, and the weather conditions for every minute of all 2,430 games. Just try getting them out of Mom’s basement once they have their hands on that!

The truly hardcore Luddites might resent being told how often the average shortstop fields balls hit 20 feet to their left at 75-80 mph, but most Non-stats Guys will appreciate the insights gained through Observational Analysis, all the more so because those insights will come from an increased focus on what is actually happening on the field.

Who will perform Observational Analysis? There are three possibilities:

Organizations such as Sportvision, BIS or others. These are the companies that can afford to hire observers, and that can survive while waiting for demand for the information to appear and grow. They will make the information available, for a price.
MLB teams. They have access to the labor needed, and a strong incentive to obtain the information ahead of their competition. They will keep their information private.
Independent analysts. There are a lot of us, and we’ve all already demonstrated a willingness to spend our free time analyzing baseball. A collaborative group along the lines of Retrosheet, sharing the workload and the fruits of their labor, might be the solution that makes the information available to the most people.

There is a fourth possibility, but I consider it unlikely: Perhaps no one will take the time to study what takes place on the field and share their insights with the rest of us. However, I feel that the potential of Observational Analysis is manifest, and I expect that it will steadily develop and eventually become as big a part of baseball as traditional stats are at present.

Here’s to the future: Observational Analysis!

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Home Run Park Factor—A New Approach

Greg Rybarczyk — 2007-06-18T04:05:15+00:00

You can’t wander too far into any of the analytically inclined baseball sites these days without encountering park factors. They are used to characterize hitting and pitching environments, so the impact of different park dimensions and other factors can be quantified and compensated for in key statistics, allowing better evaluations and comparisons of player performance.

There are different equations for park factors, but all are based on a comparison of results achieved by the home team at the park in question, along with the home team’s results achieved in other parks. One typical calculation for the park factor for home runs (HRPF) would be as follows (source: ESPN):

HRPF = ((HRhome + HRAhome)/#gameshome)) / ((HRaway + HRAaway)/#gamesaway))

The premise of this calculation is to include per-game stats for a team in its home park and on the road, and compare the two, with the difference being attributed to the team’s home park.

What’s wrong with the current HRPF calculation?

Perhaps this can be answered with a single example: in 2002, the HRPF for Chase Field (then Bank One Ballpark) was 48, and then the very next year it rose to 116. Need I say more?

This is an extreme case, but many more examples of large year-to-year swings can be found. No sane person could believe that it was really the park that changed; obviously something else is at work here. Perhaps the Diamondbacks pitchers (or the visiting pitchers) as a group surrendered fewer fly balls in 2002; perhaps the weather in 2003 was more favorable for homers (unlikely, though, since the roof at the BOB was closed for the majority of games there that year); maybe the baseballs used at the BOB were significantly livelier in 2003 than in the previous year (more on this later).

However, the most likely explanation for huge year-to-year swings in HRPF is random variation of the performances of the pitchers and hitters. The equation counts individual events authored by human beings who perform differently from one day to the next; if enough teams play enough seasons you will eventually end up with a one-year swing like the one at Bank One Ballpark in 2002-03, even without any other factors that might skew the park factor.

Unfortunately there are LOTS of uncontrolled noise factors that influence the current park factor calculations. Here are a few:

Atmospherics: conditions will vary from park to park due to differences in climate, and the weather varies from month to month and day to day at the same park.
Roster makeup: a team’s lineup might be heavily skewed towards either right- or left-handed power hitters, magnifying the impact of asymmetry in their home park.
In-season roster changes, injuries or discretionary days off: these might lead to a slugger playing more games at home than away, or vice versa.
Ball characteristics: baseballs stored in the dry heat of Phoenix will go farther than balls stored in higher humidity environments (naturally as at Dolphins Stadium, or artificially as at Coors Field)

There are two other factors that are controllable (by the league), but are currently configured to introduce significant differences between home and away home run totals:

Interleague play: because the designated hitter is allowed only in AL parks, AL teams play with a weaker lineup in about 1/8 of their away games than they do at home, while NL teams play with a stronger lineup in one-eighth of their away games than they use at home.
The unbalanced schedule: because teams play more games against their division rivals than against other clubs in their league, their home/away home run numbers may be distorted. This effect is most pronounced when all of a team’s division rivals play in parks that are on the opposite side of 100 on the HRPF spectrum (as was the case in 2006 for Boston, Chicago (AL), Texas and Philadelphia). That team’s HRPF trend (calculated the traditional way) will be magnified, e.g. Boston’s low HRPF is made even lower because their AL East rivals all play in high HRPF parks.

Multi-year Park Factors: Still Flawed

A much better way of judging a park’s propensity for home runs is to use a multi-year average. A multi-year park factor washes away many of the problems listed above, but not all of them.

The extreme individual performances (e.g. Cody Ross’ three-homer game on Sept. 11, 2006 at Dolphins Stadium) will largely balance out, but the roster makeup factor (left-handed sluggers at Yankee Stadium, right-handed sluggers at Fenway Park) will typically still be there, as will the ball characteristics factor. And of course, interleague play and the unbalanced schedule are still significant factors.

Weather effects will typically be reduced, but not entirely removed; day to day variation will be smoothed out, but some weather patterns run on cycles longer than a season. Finally, the building of numerous new ballparks, and recent fence changes on existing parks (Citizens Bank Park, PETCO Park, Miller Park, Comerica Park, Kauffman Stadium, Minute Maid Park) render some of the multi-year park factors problematic for the near-term future.

Overall, we should expect and demand more of a park factor. So, if we’re thinking about improving the HRPF metric, what would we change? Some thoughts:

HRPF should not change if the park itself hasn’t changed.
HRPF should describe the impact of the park itself, not the performances of the players who reside in it or visit it.
HRPF should incorporate atmospheric factors such as wind and temperature that change from game to game.
HRPF should include individual sub-factors for different hit directions (LF, CF, RF) rather than different hitter types (LH, RH).

One possible approach would be to use a carefully calibrated hitting machine to “hit” balls in each park, much as the PGA’s “Iron Byron” is used to test golf equipment with a completely consistent swing. Such a device would be an improvement, but apart from the time and expense of traveling to all 30 MLB parks to do this testing, a physical test would still leave the noise factors of temperature and wind uncontrolled.

A better idea would be to conduct the tests virtually, where the noise factors can be eliminated, leaving only the “signal” we seek. This can be done if we have just three things:

A fully controllable baseball trajectory simulator that includes atmospheric conditions.
Accurate scale models of all 30 MLB parks.
Someone with a blatant disregard for his own social life and circadian rhythms to operate it.

HRPF Calculation using Hit Tracker

Hit Tracker in its usual form uses observations of hit outcomes (landing point, time of flight) to derive the hit’s initial parameters (Horizontal Launch Angle or HLA, Vertical Launch Angle or VLA, and Speed off Bat or SOB, with spin assumed to be a function of these factors). But, with a few lines of code added, it becomes "Hit Whacker," using HLA, VLA, SOB and atmospheric inputs to generate a hit’s outcome. With this capability, we can create a procedure for assessing how easy or hard it is to hit homers in any park.

To cover the range of possible batted balls that could become homers, I created a “test set” of trajectories, representing 45 different HLA’s (every two degrees from foul line to foul line), 41 different VLA’s (15 to 55 degrees) and 26 different SOB’s (95 to 120 mph). That’s 47,970 different fly ball paths! I ran this complete test set in each park, in that park’s actual altitude, in the park’s average game time temperature from 2002-06, with no wind (I’ll describe how to account for different winds shortly). The trajectories were evaluated as “home run” or “not home run”, and the results were compiled.

To most accurately characterize the overall difficulty of homering in a given park, it was necessary to weight the test set of trajectories so their directions (VLA and HLA) matched the distribution of balls hit by MLB hitters. For VLA I used a combination of Hit Tracker data and the league-average GB/LD/FB percentages available on the Hardball Times site. At this point the park factors by field (RF, RCF, CF, LCF, LF) were calculated. Then I used the distribution of batted balls to those different fields, from analysis of Hit Tracker data, to generate weighting factors for horizontal direction, and used these factors to arrive at an overall HRPF for each park on a “100 is average” scale.

Finally, I multiplied all the numbers by 0.97 to account for the difference between no wind and average wind. I derived this 0.97 factor by re-running the entire simulation with average wind in each park—essentially it means that the cumulative effect of wind in MLB parks over calm weather is to increase homers by 3%.

So, to the results (remember, no wind yet!):

Park                        LF     LCF      CF     RCF      RF   Overall Avg. Temp
Coors Field                131     121      90     145     118     118      72.5
Great American Ball Park   117     114      79     132     133     111      75.0
U.S. Cellular Field        119     114      71     118     124     106      69.7
Tropicana Field            126     116      56     119     126     106      72.0
Metrodome                  113     110      70     130     127     106      70.0
Miller Park                100     116      70     131     137     105      74.3
Citizens Bank Park         118     116      66     119     122     105      75.5
Yankee Stadium             115     100      72     128     134     105      71.9
Minute Maid Park           132     116      37     114     135     105      78.6
Jacobs Field               108     109      76     117     129     104      69.7
Camden Yards               107     121      75     110     109     102      79.5
Rangers Ballpark           101      99      77     119     120      99      83.3
Rogers Centre              120      96      64      96     120      97      73.2
Dolphins Stadium           119     106      59     101     107      97      83.3
Turner Field               108     104      78      91     114      97      78.5
Angels Stadium             103      94      81     114     104      96      73.3
Busch Stadium III          103     100      75     102     106      94      77.5
Shea Stadium               101     112      56     112     101      94      72.1
Safeco Field                94      89      64     115     131      93      67.1
PNC Park                   102      86      76     105     114      93      72.8
Chase Field                108      93      45     115     112      91      80.1
PETCO Park                 107      97      72      79     107      91      69.3
Comerica Park              106      98      35     104     127      91      70.6
Dodger Stadium              95     100      73     100      95      90      71.7
Fenway Park                105     106      57      94      88      89      69.4
McAfee Coliseum             98      97      66      96      98      89      65.2
Wrigley Field               86     122      61      98      86      89      70.0
Kauffman Stadium           101      91      60      91     101      87      77.7
AT&T Park                   98     100      56      59     106      83      64.3
RFK Stadium                102      79      51      79     102      81      77.2
Average                    108     104      66     108     114      97      73.5

The highest overall rating belongs to Coors Field, driven by its higher altitude. The simulation used the same baseball characteristics for all 30 parks, but this might not be appropriate here, due to Colorado’s infamous humidor. We don’t have recent data on un-humidified balls at Coors Field, but if the humidor does as advertised and restores the baseballs to a “normal” state (rather than an extra-soggy state), then the calculated PF here should be accurate.

The easiest individual field for homers is RCF at Coors, with a rating of 145, while the most difficult field is center field at Comerica Park, with a PF of 35. St. Louis Cardinal Chris Duncan’s May 20, 2007 homer over the center field wall in Detroit was the first to clear that fence since at least 2005, before Hit Tracker was in operation; home runs to that field are even less common than the 35 rating suggests, probably due to Tigers hitters adjusting their swing to avoid hitting fly balls to center field. Duncan missed that memo, obviously…

Chase Field “plays” easier for home runs than its calculated HRPF suggests; Hit Tracker data may explain why. So far in 2007, the average standard distance (which is independent of atmospheric effects) for home runs hit at Chase Field is 394.3 feet, while the league average standard distance is 391.4 feet. For Arizona hitters, the effect is even more striking, with D-Backs hitters averaging 395.7 feet at home and 383.5 feet on the road, an amazing 12-foot difference.

Perhaps such an effect might come about due to random chance on a small sample size, but here’s one statistic that is quite convincing: Arizona’s top ten longest homers by standard distance so far this year were ALL hit at Chase Field. The odds of that happening by chance are more than 1,000-to-1 against. So, we can either believe that the Diamondbacks just happen to slug like Babe Ruth at home for no particular reason (e.g. Tony Clark and Eric Byrnes, who own the two longest MLB homers so far in 2007, both hit on May 8th at Chase), or we can believe that the ball is livelier in Phoenix, probably due to the hot, dry conditions in the Valley of the Sun…

How to Account for Temperature and Wind

The HRPF’s have been calculated with no wind at average park temperatures, but changes in the atmospherics (wind and temperature) obviously have to be considered when trying to accurately describe how easy or hard it is to hit one out of a park. Unfortunately, there are nearly infinite combinations of wind and temperature that can exist during a game, and it is therefore prohibitively time consuming to simulate them all, when each run takes about 30 minutes. Instead, I ran a series of “sensitivity” runs to gauge the impact of temperature and wind conditions on the calculated park factors, and was able to derive average adjustment factors for the HRPF’s.

For temperature, add or subtract 0.26 for each degree of temperature difference between the game-time temperature and that park’s average game-time temperature (see the data table for the average park temperatures.) For wind blowing straight in or out, add or subtract 1.9 for each mph of wind. For wind blowing diagonally 22.5 degrees in or out, add or subtract 1.7 for each mph of wind. For wind blowing diagonally 45 degrees in or out, add or subtract 1.4 for each mph of wind. For wind blowing 67.5 degrees in or out, add or subtract 0.5 for each mph of wind.

Example: Wrigley Field has average game-time temperature of 70.0 degrees, and HRPF’s of 86/122/61/98/86 for LF/LCF/CF/RCF/RF, respectively, with a total HRPF (no wind) of 89.

On a 40-degree day with the wind blowing in from CF at 10 mph, the HRPF’s will be adjusted as follows:

For temperature, adjust each PF by 30*-0.26 = -7.8 or -8.
For wind, adjust the CF PF by 10*-1.9 = -19. Adjust the RCF and LCF PF’s by 10*-1.7 = -17. Adjust the LF and RF PF’s by 10*-1.4 = -14.
The adjusted PF’s will be 64/97/34/73/64, for an overall HRPF of 67.

On a 90 degree day with the wind blowing out to RF at 20 mph, the HRPF’s will be adjusted as follows:

For temperature, adjust each PF by 20*0.26 = 5.2 or +5.
For wind, adjust the RF PF by 10*1.9 = +38. Adjust the RCF PF by 10*1.7 = +34. Adjust the CF PF’s by 10*1.4 = +28. Adjust the LCF PF by 10*0.5 = +10. The LF PF is essentially unchanged by the wind blowing out to RF.
The adjusted PF’s will be 91/137/94/137/129, for an overall HRPF of 115.

For simplicity’s sake, I have created one temperature factor and one set of wind factors to be used in all stadiums. The temperature adjustment factor is dependable, but the wind adjustment factors should be considered approximate, because of the different shielding effects of the grandstands in the different parks.

In parks where the outfield is fairly low and open (e.g. Wrigley Field, Fenway Park, Kauffmann Stadium), the wind factor will be most accurate, while for well-shielded stadiums (e.g. Rogers Centre, McAfee Coliseum, Rangers Ballpark in Arlington, any of the retractable-roof parks when open), the standard, one-size fits all wind factor may not describe the wind impact accurately. In particular, Rangers Ballpark in Arlington has become known for swirling winds that aid batted balls at low levels even as the high-level winds howl in from the outfield most of the summer.

HRPF’s generated by Hit Tracker should remain valid for the current MLB parks as long as the fences don’t move, and in the future, this park evaluation method will be used on new stadiums in Washington D.C., the Bronx, Queens and elsewhere.

Visit the Hit Tracker site to download the Hit Tracker Home Run Park Factor Calculator for easy calculations of HRPF’s for all 30 ballparks in any weather conditions.

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Sometimes It Is How Far You Hit Them

Greg Rybarczyk — 2007-04-04T04:03:15+00:00

It’s true that no matter how far you hit a home run, it’s still only worth one run. However, people still want to know who hits them the farthest and the hardest. Using an analytical method and tool called Hit Tracker, I was able to show just that by compiling a complete record of long balls struck during the 2006 MLB season.

During the off-season, it struck me that it might also be interesting to know which hitters made a habit of sneaking the ball just over the fence instead of blasting it into the cheap seats. While a tape-measure homer is likely to be a home run no matter what, the ones that barely clear the wall are very susceptible to the influence of wind and temperature, and sometimes also depend on the leaping ability (or lack thereof) of the outfielders.

These shortest of homers are also the sort of hits that could have turned out very differently in another ballpark, had they only been struck on a different day. I wondered, how much of a player’s home run total might be attributable to good or bad luck, as opposed to the player's underlying skills and strength?

To satisfy my curiosity, I started by classifying every home run into one of three categories, as follows:

1. "Just Enough," or "JE": Any homer which cleared the fence by 10 vertical feet or less, OR whose landing point was within one fence height of the fence (e.g. less than seven feet past a fence that is seven feet tall). This category also includes inside-the-park homers.

2. "No Doubt", or "ND": Any homer that cleared the fence by at least 20 vertical feet AND whose landing point was at least 50 feet past the fence.

3. "Plenty," or "PL": all other homers.

A few interesting anecdotal pieces of data emerged right away:

Jim Edmonds of the Cardinals hit 19 homers in 2006, only one of which was of the “Just Enough” type. Perhaps his own experience at robbing his fellow major leaguers above the fence led him to put a little extra on his own homers, just to be sure.
Washington’s Alex Escobar knocked four balls over the wall in only 87 ABs, part of a fairly promising .356/.394/.575 injury-shortened season, but all four of his homers were JE’s that barely cleared the fence. Turn those four into long flyouts (a worst-case scenario, admittedly) and his line would be .310/.354/.391.
Among the 19 homers allowed by Cincinnati’s Kirk Saarloos were seven No Doubters but only one JE.
Seattle/Philadelphia pitcher Jamie Moyer served up 33 homers in ’06, but only one flew far enough past the fence to reach the “No Doubt” category. Consider Moyer’s “crafty veteran” credentials renewed.

Beyond the anecdotes, it turns out that when you look at the entire league’s data together, analysis of how far home runs carry beyond the fence can provide some interesting insight into how much of a player’s home run production from last year was due to good fortune that might not shine on him again this year.

When we examine the 2006 season data (which is available here), we find that the MLB average breakout of home runs by type was 27% JE, 55% PL and 18% ND. For hitters with above average power, the ratio shifts to 25%/55%/20%, and for the elite sluggers, the ratio becomes 23%/54%/23%, as “No Doubt” homers make up a larger fraction of their output.

A player should hit his home runs according to these ratios, given enough home runs to smooth the results. Unfortunately, even Ryan Howard and David Ortiz didn’t hit enough homers to reliably generate a smooth output, so we need to decide what it means when an unusual split exists among the three home run types.

Because fly ball distances follow a roughly normal distribution, if a player hit a lot of ND homers, that player should also hit a lot of PLs and a lot of JEs, given enough at bats and barring any unusual run of bad luck or frequent encounters with home run thieves like Torii Hunter or Andruw Jones. Similarly, when a player doesn’t hit many NDs, he should not hit many JEs either. This means that the ratio of ND homers to JE homers is a critical indicator.

JE homers are the ones that can swing a players HR totals: A little luck, good or bad, can make the difference between a home run and a warning track flyout. Those who hit a lot fewer JE homers than expected (e.g. Adam Dunn, who hit only two JE homers against 16 NDs) are likely to be luckier this year, and thus tally more home runs. Those who hit a lot more JE homers than expected (e.g. Brian McCann, who hit 11 JE homers and only 1 ND) are likely to suffer worse fortune this year, and end up with fewer homers overall.

The plot below shows the percentage of “No Doubt” homers for every player who amassed at least 300 ABs and hit at least 20 HRs in 2006. The black line is a best fit line; its slope represents the increase in expected ND percentage as home runs per 500 AB increases. Some notable players are highlighted on the plot.

The next plot shows the percentage of “Just Enough” homers for every player who amassed at least 300 ABs and hit at least 20 HRs in 2006. Some of the same names appear on this plot, once again far from the best fit line.

Because of the small sample sizes involved, we should be cautious about drawing conclusions. However, some players’ home run “splits” turned out far enough from the typical ratios that a regression back towards the mean can be expected.

A metric for describing how closely a player follows the “typical” splits is the ratio of ND to JE homers. The league average is 0.67, and for sluggers with > 30 HR per 500 AB, the ratio is 0.92; we will consider any hitter with > 1.50 to have a strong likelihood of hitting more homers, while anyone with < 0.33 is a strong candidate to hit fewer homers in 2007.

Upside List: (Number of HRs JE/PL/ND, ratio = ND/JE)

Moderate:
Jim Thome: 8/21/13, ratio = 1.63
Travis Hafner: 7/23/12, ratio = 1.71
Carlos Beltran: 6/25/10, ratio = 1.67
Albert Pujols: 8/27/14, ratio = 1.75
Manny Ramirez: 6/18/11, ratio = 1.83
Aramis Ramirez: 6/21/11, ratio = 1.83

High:
Jason Giambi: 3/26/8, ratio = 2.67
Alex Rodriguez: 3/23/9, ratio = 3.00

Very High:
Jonny Gomes: 3/7/10, ratio = 3.33
Adam Dunn: 2/22/16, ratio = 8.00
Mike Jacobs: 1/10/9, ratio = 9.00

Downside List:

Moderate:
Alfonso Soriano: 18/23/5, ratio = 0.28
Josh Willingham: 12/11/3, ratio = 0.25
Nick Johnson: 8/13/2, ratio = 0.25
Brad Hawpe: 8/12/2, ratio = 0.25
Bill Hall: 15/17/3, ratio = 0.20
Nick Swisher: 14/18/3, ratio = 0.21
Justin Morneau: 15/16/3, ratio = 0.20
Jacque Jones: 11/14/2, ratio = 0.18 (though perhaps we should give Jones a break, as one of his JEs was a 445-foot blast to dead center field that just cleared Tal’s Hill at Minute Maid Park!)
Rich Aurilia: 7/15/1, ratio = 0.14

High:
Ramon Hernandez: 8/14/1, ratio = 0.13
Adrian Beltre: 8/16/1, ratio = 0.13

Very High:
Ryan Zimmerman: 9/10/1, ratio = 0.11
Brian McCann: 11/12/1, ratio = 0.09
J.D. Drew: 4/16/0, ratio = 0.00

Notice that the hitters with the highest ratios are mostly guys already known for their prodigious power, so it may seem strange to suggest that there is upside to their 2006 power numbers, but most other sluggers follow the typical ratios fairly well: Howard hit 10/35/13, Ortiz hit 11/31/12, Lance Berkman hit 11/21/13, Frank Thomas hit 10/22/7, Jermaine Dye hit 10/22/12, etc. The hitters on the upside list were unlucky in 2006, and will probably enjoy better luck this year.

The hitters with the lowest ratios are mostly guys who had career years in terms of power: Among the 14 players on the list, only Aurilia, Drew and Beltre did not set or match career highs in homers last year. However, lots of other hitters had career years while conforming to the typical ratio: Matt Holliday hit 8/17/9, Adam LaRoche hit 6/20/6, Raul Ibanez hit 7/21/5, Prince Fielder hit 7/14/7, Craig Monroe hit 4/20/4, etc. The hitters on the downside list, immensely talented hitters all, nevertheless owe a portion of their outstanding performances from a year ago to good fortune (as is usually the case when a player has a great year). They probably won’t enjoy the same luck in 2007.

Thus far we’ve narrowed the field of consideration to those who amassed 300 ABs and 20 HRs, and focused only on the blatant statistical outliers, in order to minimize the likelihood of being misled by small sample size effects. However, a few more names did stand out among those who did not meet the minimums listed above. David Ross (2/13/6), Conor Jackson (1/7/7) and Edwin Encarnacion (1/10/4) seem to have gotten less than their share of JE homers, and thus should do better in 2007. Joe Mauer (10/3/0) and Jhonny Peralta (11/2/0) both made a living in 2006 dropping homers just over the fence; in 2007, we should expect fewer of their long flies to make it out.

Similar lists can be compiled for pitchers, reflecting how some pitchers gave up fewer JE homers than expected, and some more than expected, based on the proportions of the different types of home runs they allowed. Due to the smaller sample sizes, I will stick to a single upside and downside list:

Upside List: (Number of HRs JE/PL/ND, ratio = ND/JE)

Kyle Lohse: 8/7/0, ratio = 0.00
Sean Marshall: 10/9/1, ratio = 0.10
Paul Maholm 9/9/1, ratio = 0.11
Jose Contreras 9/10/1, ratio = 0.11
Joel Piniero 9/12/2, ratio = 0.22
Jason Bergmann 7/4/1, ratio = 0.14

Downside List:

Kirk Saarloos: 1/11/7, ratio = 7.00
Ryan Madson 2/12/6, ratio = 3.00
Scott Kazmir 3/4/8, ratio = 2.67
Taylor Buchholz 4/8/9, ratio = 2.25
Brett Myers 3/20/6, ratio = 2.00

Visit the Hit Tracker site for trajectory analysis of home runs and other batted balls throughout the 2007 season.

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