Of 132 starting pitchers that have thrown at least 100 innings this season, Brewers’ right-hander Yovani Gallardo has the third lowest O-Swing% in the majors at 24.8%.  That means opposing hitters swing at 24.8% of his pitches that are outside the strike zone.

Even Nick Blackburn and Brian Bannister get more swings at pitches outside the zone.

Common sense suggests the better a pitcher’s repertoire, the more hitters will chase bad pitches.  Therefore, we would expect Yovani Gallardo to have below-average stuff and a lower strikeout rate because he gets fewer hitters to chase bad pitches.

That is, we would expect that to be the case if we merely looked at Gallardo’s O-Swing% in a vacuum.

As it turns out, Gallardo gets very few hitters to swing at pitches outside the zone because hitters rarely swing in general with Gallardo on the mound.  His 40.8% swing rate is the fourth lowest in the majors (min. 100 innings).  Not only that, but hitters also do not make contact often with his pitches when they do swing – as evidenced by his 79.6% contact percentage and 8.3% swinging-strike percentage.

We can infer from these numbers that hitters flat-out do not see Gallardo’s pitches very well from the mound.  Hitters do not swing often – and when they do, they do not make much contact.

In short, Gallardo has well above-average stuff.  His 92-94 MPH fastball is a plus-pitch, and his spike curve can be deadly when he is not spiking it before it reaches the plate.  Moreover, Gallardo has developed a slider, which is arguably becoming his best pitch.

So, how can Yovani Gallardo transform himself from a top-tier #2 pitcher to a full-fledged ace?

The answer appears to be simple.  Throw more strikes.

Of the starting pitchers that have thrown at least 100 innings in 2010, Yovani Gallardo throws the 7th fewest balls in the strike zone. (Livan Hernandez is predictably number one).  That high-percentage of balls outside the strike zone would be acceptable, but as we have established earlier, Gallardo does not induce many swings outside the zone.

That obviously leads to higher walk rates, higher pitch counts, and lower innings totals.  Those are all things that must change for Yovani to be a true ace in Major League Baseball.

Many of you are likely thinking: “Of course Gallardo should throw more strikes. That is an obvious statement.”

Not necessarily.

Some pitchers, such as Livan Hernandez and Jamie Moyer, live on throwing balls outside the strike zone.  They do not have good enough “stuff” to live by throwing strikes.  They have contact percentages north of 92% on balls inside the zone, so they bait hitters and get them to swing at poor pitches.  Both of them have literally made a career avoiding the strike zone

Gallardo is not that type of pitcher.  It seems opposing hitters have decided their best chance to reach base is to actually not swing at all, merely hoping the upcoming pitch is a ball – which it is 57.4% of the time.  That accounts for the high pitch counts, the low walk totals, and the low inning totals.

The only worry about Gallardo throwing more strikes is that more balls would then be put in play, which may not be a positive outcome with Milwaukee’s below-average defense.  It could effectively be argued that Gallardo is better suited to stick to walks and strikeouts – though the Brewers’ front office should work to solidify the defense this winter.

Throwing more strikes would lower the pitch counts, lower the walk rates, and increase the number of innings pitched without sacrificing production on the mound.  Opposing hitters do not make much contact whether or not Gallardo throws the ball in the strike zone, so he may as well cut the walk rate and work in the strike zone much more often.

His development of a slider should aid that mission.  That spike curveball cannot be thrown consistently for strikes, but his new slider (sometimes cutter) can be thrown for a strike on any count.

Perhaps that is the missing piece that can help Yovani Gallardo transform himself into a bona fide ace for the Brewers.  Or perhaps the right-hander simply needs to make a conscious effort to not nibble.

Whatever the case may be, everyone certainly knows the Brewers organization could certainly use an ace.  Milwaukee’s success in 2011 may hinge on whether or not Gallardo is able to take the next step in his development – which seems to be consistently throwing more strikes in every start.

Adam Wainwright’s strikeout rates keep increasing. In over 400 innings in AA and AAA during his age 21-23 seasons, his strikeout rate was 7.8 per nine innings. When he was elevated to the majors in 2006 as a relief pitcher his strikeout rate took an expected jump to 8.64 K/9. At the time he was throwing his curveball 25.9% of his pitches. A return to starting the following year led to a decrease in both his curveball use (18.6% in 2007 and 17.9% in 2008) and his strikeout rate (6.06 K/9 in 2007 and 6.20 K/9 in 2008).

In 2009, Wainwright made a change in his pitch selection, reverting back to the curveball percentages from his bullpen tenure. The increase in curveball use (24.0% in 2009) increased his strikeouts per nine to 8.19 and turned him from an above-average pitcher (3.90 and 3.78 FIP in 2007 and 2008, respectively) to a Cy Young contender (3.11 FIP). The increased use of the curveball in 2009 also increased its effectiveness, doubling to 2.71 wCB/C. The effectiveness on his slider tripled. Unfortunately, his fastball decreased in effectiveness, going from essentially average to -.75 wFB/C.

In 2010, Wainwright has taken his curveball use to another level, increasing to 28.5% of his pitches and his strikeout rate to 8.26 K/9 and lowering his FIP to 2.86. He has not sacrificed control, lowering his walk rate to 2.21 BB/9. His curveball and slider, which may be more of a cutter, have been slightly less effective, but still very useful pitches. The significant change has occurred in the effectiveness of his fastball. Wainwright has decreased the number of fastballs thrown to a career low 46.5% of pitches. With this decrease has come a drastic increase in the effectiveness of the fastball without changing the velocity, moving to 1.00 wFB/C from last year’s total of -0.75 wFB/C.

Also of note, increasing his strikeouts has not affected his efficiency, with 15.7 P/IP in 2007, 14.8 P/IP in 2008, 15.5 P/IP in 2009 and a career low 14.6 P/IP in 2010.

Wainwright has remained effective this season throwing the third highest percentage of curveballs of any pitcher. (Only Wandy Rodriguez and Gio Gonzalez have thrown a greater percentage of curveballs this year.) When you have the curve he has, you can’t blame him. The consequence, whether inteneded or not, is a sea change in the effectiveness of his fastball.  Another Cy Young-caliber season at a bargain price for the Cardinals.

Earlier in the year I observed that CC was getting significantly more groundballs than earlier in his career. At this time we can see that he has maintained this new approach throughout the year (via fangraphs):

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Over the last couple of weeks there has been a lot of debate about the value of home field advantage in baseball. The discussion was crystallized most recently when the Yankees rested key bullpen members in their first place showdown against the Rays, but it has really be going on since the advent of the Wild Card.

Yesterday, we wondered about the value of finishing first from an accomplishment perspective, but ultimately, that is a very intangible way of looking at the question. At SI.com, Joe Seehan looked at home field advantage from a competitive standpoint and came to the conclusion that it really wasn’t an advantage at all. According to Sheehan’s research, the number one seed has advanced to the World Series in only eight of 24 chances since 1998, when the current playoff format was established. What’s more, over that span, the home team has only gone 45-39 in all post season series, according to Sheehan. In other words, there really isn’t a home field advantage in baseball during the postseason.

There is at least one more vantage point from which to consider this question, and it could very well be the most important: economics.

In the post season, gate revenue (i.e., attendance) is divided between the players and hosting team using the following format:

• Players: 60% of gate receipts from first three games of LDS and first four games of LCS and World Series; no contribution from other games.
• Home team: 40%* of gate receipts from first three games of LDS and first four games of LCS and World Series; 100% of gate receipts from all other games.

*A small percentage (approximately 1.5%) of LDS gate receipts goes to the umpires, while 15% of LCS and World Series gate receipts go to MLB.

On the face of it, there seems to be an economic advantage to having home field. But, is it real, and if so, how significant is it?

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Again looking at leaderboards, I noticed that only one of the top 10 hitters by batting average has fewer than 10 home runs; that would be your 2009 American League MVP, Joe Mauer.

Mauer is hitting .324 through 127 games and, barring a disaster to end the season, will finish as a 5-WAR player.  It’s a far cry from his 8-win 2009 season (which no one could reasonably expect him to repeat), and just looking at his lines across the two seasons, it’s not hard to see where the difference is.

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Since 1994, when Major League Baseball converted to three divisions and expanded its playoff system to four teams per league, the format has been a mixed blessing.  Good teams that didn’t win their division would qualify for the playoffs as a wild card team, and the expansion of two more playoff teams per league meant that more fans could root for their teams to make the playoffs.  Gone were the days of a make-or-break pennant race, as the second-best team in the division didn’t make the playoffs. 

While I am not arguing that this system is better than what was done in the past, our current format is not the most optimal way MLB should have the playoffs structured, and I propose an alternative.  First, I would like to review what I feel is wrong with the current system:

Problem #1: Teams are not incentivized to get the top seed.

The reward of having the league’s best record means little if a team knows they’re making the playoffs.  For example, let’s say it is the last day of the regular season and a team is either tied for the division lead or for the best record, but if it loses the last game it will be the wild card team or the second seed.  What incentive is there to win that game?  At the risk of overusing them, a manager is unlikely to use an ace on two- or three-days rest, or his closer for two or three innings.  In other words, there is no urgency for teams as to where they’re seeded in the playoffs. Regardless of who wins the division, both teams should make the playoffs.

Problem #2:  Home field advantage is not much of an advantage.

Since 1995, home playoff teams have won two-thirds of NFL games, 65% of NBA games, but only 54.6% of MLB games.  (Incidentally, home teams won 54.1% of regular season MLB games during the same period.)

Problem #3:  Wild card teams have performed just as well – if not better than – the division winners in the playoffs.

Nine of the thirty pennant winners have been wild card teams.  Given that wild card teams do not have home field advantage in either the division series or the LCS, this shows that the current system does not put wild card teams at much of a disadvantage.

Simply put, the current “balanced” playoff system was easy to implement  and simple on the schedule makers – the higher seed gets home field advantage over the lower seed.  While there has been talk of different ways of unbalancing this, not much has changed, although there is talk of changing the Division Series to a best-of-seven series.

My proposal, which improves upon the issues mentioned above and makes for more exciting pennant races and playoff games, is as follows:

I propose that MLB adds a second wild card team to both leagues, and that both wild card teams in each league play a one-game play-in game the day after the regular season ends to determine the fourth seed.  The top-seeded team in each league – based on the regular season record – would then play the four seed in a best-of-five Division Series.

This change eliminates the problems listed above.  Teams would now have an incentive both to 1) try for the top seed and 2) avoid being a wild card.  Top seeds are given a big advantage in this scenario because they will face the winner of the playoff game that will most likely have used their ace in either that game or a pivotal last game of the season, and wouldn’t be able to use him again until possibly Game 3 of a five-game Division Series.  This comes at a great detriment to the wild card teams and more than makes up for the “advantage” of home field for the top seed.

Taking this year’s AL East race as an example, if both the Yankees and Rays are tied going into the last game of the season knowing they’re both making the playoffs, there is little incentive for either manager to push their players that last day.  But if either team knew that the second-place team would, say, face Boston in a one-game playoff, then this would greatly change managerial decisions that last day of the season.

Given that both proposed one-game playoffs would only last one day, scheduling the playoffs will not make it much of a burden on the other playoff games.  As we saw with the Chicago-Detroit game last year, one-game playoffs are exciting to watch and would be a great opening to the MLB playoff season.

We’ve all heard a broadcaster comment on the impending doom of a leadoff walk and yet they fail to seem to apply the same sort of fateful outcome for a single. I thought it would be interesting to find the outcomes of each of the ways a player can leadoff an inning by getting on first base and see if it affects whether or not the runner goes on to score. I took the retrosheet data sine 1952 (but not including this year) that I have as a MySQL database and created a quick python script to determine these results. I took it further and examined if the breakdown were any different in late game situations, as I’m always hearing “You never want to walk the leadoff batter but especially late in close ball games”. I was also curious if even in general more solitary runs get manufactured once a leadoff runner gets on base in late game situations.

Total times batter lead off an inning by getting to first: 508312
Total times runner scored: 192150

So a leadoff batter who starts on first base scores 37.80% percent of the time, here is the breakdown via the means they get aboard

Any inning

Single      325455 Scored 122662   37.69%
Walk        150570 Scored  57189   37.98%
HBP          11865 Scored   4600   38.77%
Error        19260 Scored   7270   37.74%
Strikeout     1007 Scored    375   37.24%
Catcher's Int. 155 Scored     54   34.84%
Totals      508312 Scored 192150   37.80%

So it appears as though it’s not much of a statistically significant difference between the walk and the single. The HBP numbers seems to be a bit of an outlier, I’m wondering if that is just sample size or if such an outcome rattles the pitcher to the point of that much more runs being produced.

Lets now examine the breakdown based upon the stage of the game.

6th inning or earlier

Single      217421 Scored  83243 38.29%
Walk        100587 Scored  38798 38.57%
HBP           7879 Scored   3070 38.96%
Error        12778 Scored   4880 38.19%
Strikeout      645 Scored    244 37.83%
Catcher's Int. 107 Scored     36 33.64%
Totals      339417 Scored 130271 38.38%

7th inning or later

Single     108034 Scored 39419 36.49%
Walk        49983 Scored 18391 36.79%
HBP          3986 Scored  1530 38.38%
Error        6482 Scored  2390 36.97%
Strikeout     362 Scored   131 36.19%
Catcher's Int. 48 Scored    18 37.50%
Totals     168895 Scored 61879 36.64%

Interesting how 1.74% more leadoff runners reaching first score in the earlier innings.  Is this a comment on the failure of manufacturing runs or pitching being different in the later stages of the game?  Perhaps a deeper look based upon “close game situations” is in order for that.

The Kansas City Royals last week flashed back to the past by signing NFL quarterback Pat White in a move that conjures up memories of the old Kansas City Royals Baseball Academy which I wrote about here in June.

Judging by the internet commentary on this move, it is being seen as comical or a case of pure desperation by either White or the Royals and is being dismissed entirely by baseball and football fans alike.

For those unfamiliar with White, he was a star college quarterback for West Virginia for several years and noted for his unique combination of running and passing ability. White is rather short for an NFL quarterback and lacked the great arm strength desired by NFL scouts. Many thought he would be best suited as a wide receiver and might be converted by a team like fellow college quarterbacks Antwaan Randle-El  or Patrick Crayton and many others have done successfully.

The Miami Dolphins thought differently and drafted him in the second round with plans to use him in their much-hyped “Wildcat” formation which was wildly successful in its first year of operation.

A year later, the Dolphins infatuation with White has apparently waned and he was released as an NFL camp casuality in his second year. This posed a problem for White because a team that might want him as a WR cannot place him on their practice squad because he has too much NFL service time and no teams are interested in making him a backup quarterback right at the beginning of the season.

White has long been pursued by major league baseball teams, being drafted by the California Angels in the fourth round out of high school in 2004. He was subsequently drafted again by the Angels,  Reds and Yankees in the last part of the draft.

As a high schooler, White projected as a Carl Crawford-type outfielder with the speed to play center field. He hit .487 his senior year of high school and might have been taken in the second round if not for his quarterbacking abilities.

Now, the question is can Pat White play baseball at age 24? Most seem to think not. It’s a question of Nature or Nurture. And most seem to come down on the Nurture side of things.

Are baseball players born or developed? Has Pat White’s window of opportunity closed because he hasn’t swung the bat for so long or is his brain arranged in such a way that he was born to hit a baseball?

There is reason to think it is possible that White could be playing in the major leagues in a few years. Consider the case of Ron LeFlore.

LeFlore did not grow up playing baseball or any sport for that matter. He was incarcerated at age 19 and began playing sports because he noticed that the prison athletes received extra free time from the guards to play their sports. He began by playing basketball and then was invited to play softball, ultimately graduating to the prison baseball team where he began playing at age 23.

In a community service visit to the prison, Tigers manager Billy Martin was cornered by the inmates who urged him to give LeFlore a tryout. Martin promised he would. Upon his release from prison, LeFlore unexpectedly took Martin up on that promise. Martin didn’t really remember LeFlore but made good on his pledge and ultimately signed him to a contract over the objections of Tigers ownership after a prodigious batting practice display. It was real-life shock and awe.

And how much did LeFlore’s late start set him back? He was signed at age 25 and hit .273 in a 73 at-bat stint in Class A. The next year, he hit .339 in 423 at-bats in Class A and was promoted to AAA and the Tigers shortly afterward. Just one year after signing and three years after beginning playing the sport, LeFlore hit .260 in the major leagues and eventually became a .300 hitter and stole as many as 97 bases in a season.

Or consider Rick Ankiel who went back to hitting at age 25 when he could no longer throw a strike and eventually hit 25 home runs for the St. Louis Cardinals at age 28.

Or consider the way Deion Sanders and Bo Jackson tore through the minor leagues despite never really being all that serous about baseball.

There is also Josh Hamilton. You know him, right?

As talented as White may be, my guess is that he does not have the baseball talents of a Ron LeFlore. I suspect that White is not committed enough to baseball and will bolt for his first chance in the NFL or professional football. I will not be at all surprised if White manages to hit Class A pitching rather well, though.

I’ll put White’s odds at being a major league baseball player at 1 in 15. However, if White was truly destined to be a All-Star major-league baseball player and was another Carl Crawford, he’ll be playing center field for the Royals in three years. No joke.

It’s been done before — more than once.

A few weeks ago, Sportvision hosted the 3rd Annual Pitchf/x Summit.  Sportvision is the company behind the Pitchf/x system and has initiated Fieldf/x, which I’ll get into in a minute.  The goal of the summit was to share some of the research being done in baseball analysis, while also serving to explain the possibilities that exist with the new system.  Without further ado, here were the presentations:

Using Velocity Components to Evaluate Pitch Effectiveness (Matt Lentzner/Mike Fast): The purpose of this study was to change the reference point by which Pitchf/x data are measured.  Often, fastballs show more movement than breaking balls, but without the proper frame of reference, it means nothing.  Mike and Matt were able to demonstrate how to determine the horizontal and vertical velocities with respect to the batter’s eye and make the Pitchf/x data more meaningful.

Pitchf/x Application in Player Development and Evaluation (Dr. Glenn “Butch” Schoenhals): Dr. Schoenhals has a Pitchf/x system set up at his instructional school, which allows pupils (including some major leaguers) to see the their pitches broken down immediately and make adjustments.  In conjunction with three cameras set up around the pitcher, the Pitchf/x data provide benefit to both pitchers and instructors in learning/teaching how to pitch.

Okajima’s Mystery Pitch (Matt Lentzner): Hideki Okajima throws a pitch roughly 20% of the time that had previously been classified as a curveball, more specifically a “rainbow curveball.”  Actually, it didn’t really fit any of the known pitch types.  Using his research on pitch types and arm slots (“The Pitching Peanut”), we see that this pitch has almost no break, is faster than a curveball but slower than a slider, and falls at the exact center of the peanut.  His explanation: Okajima is the Boston pitcher who is actually throwing the gyroball, not his more famous teammate Daisuke Matsuzaka.

Leaving the No-Spin Zone (Alan Nathan): Dr. Nathan showed his experiments that relate the spin of the baseball just before and just after it is hit. The result? The two are almost totally independent of each other! I couldn’t believe that, but Dr. Nathan made a lot of sense.  This was a high-grade physics lesson, crashed into about 20 minutes.  He explained why balls tend to curve toward the foul lines; he showed that the bat actually “grips” the ball for a few nanoseconds or so before the ball explodes off the bat, which contrasts the earlier model of the ball “rolling” off the bat.  Really, really cool.

Fieldf/x System Overview (Vidya Elangovan): And the main event began.  Fieldf/x is a new tracking system that utilizes cameras attached to the light standards in baseball stadiums (for now, just AT&T Park) to track the movement of every person on the field 15 TIMES A SECOND.  As soon as I heard that, my mind started going crazy and I don’t think I paid attention for about 5 minutes.  The only issue at the time is that the system does not include the ball (but it will).  All ball events currently have to be added by someone watching the video.  The following presentations showed some of the things you can actually do with the data, and it’s fairly obvious that these data, particularly when connected to batted ball data through the Hitf/x database, are about to revolutionize how baseball players are evaluated.

Infield Defense with Fieldf/x (John Walsh): Actually the first presentation, thanks to being in Italy, (tough life), but it really would have been more helpful after the overview.  Either way, a lot of cool stuff.  First thing he said was that in tracking the different players, he noticed that an average centerfielder runs 8 miles per game, which stunned me and kept my attention.  Thanks to these new data, we can also see the effects of shifts and also what players away from the ball are doing while teammates are attempting to make plays.  Other questions John poses: can we see infielders cheating in a certain direction as the pitchers throws the ball? Do infielders lean in a certain direction before the pitch? Based on his initial investigations, he saw that third basemen step toward the line as the pitch is delivered and shortstops step directly at home plate.  Weird, but potentially important, and just a peak into what can be obtained.

From Raw Data to Analytical Database (Peter Jensen): As a baseball nerd and a programming dork, this was really cool.  Peter Jensen took the 400,000 lines of code that results from each game and wrote a macro to display what actually happened in the game in an Excel worksheet.  The simulation relates the position of each player as well as an approximation of where the ball is throughout the play.  His solution with regards to the reorganization of the data was very impressive for a first run, and it is absolutely vital to make the data useful for analysis.

Using Fieldf/x to Assess Fielders’ Routes to Fly Balls (Dave Allen): These next three were absolutely incredible to me (and I’m sure the last three would have joined them had I had the time to stay).  By using the data to reconstruct fielders’ routes to the ball, Allen surmises that the Fieldf/x data can be used to determine the speed of an outfielder as they pursue a ball, the starting points of each fielder at the time of the pitch (and hit), and how efficient each player is in getting to the ball (measuring the distance traveled against the shortest distance to the ball).  To me, this is something that teams can use to help players they already have by addressing alignment issues or noticing what is happening during the different points of pursuit.  Are outfielders getting good reads/jumps on the ball?  Are they running in straight lines or weaving?  Simply put, the data can confirm for us (and also measure exactly and more efficiently) what our eyes (and scouts’ eyes) have seen.

Measuring Base Running with Fieldf/x (Mike Fast): Mike’s presentation examined the different portions of base running and what the data can be used for.  Mike was able to track each base runner’s path around the bases, even what they were doing on pitches that weren’t hit (during which we would typically say “nothing happened”).  Obviously, with all of these data, there’s a lot happening.  Also, by knowing the position of the player at each moment in time, we can track both his speed and acceleration as rounds the bases; very valuable information for measuring “baseball speed.”

Fieldf/x of Probabilities: Converting Time and Distance into Outs (Jeremy Greenhouse): The coolest of the presentations.  As soon as he said the words “probability model,” I was sold.  Jeremy first examined stolen base attempts (in the thirteen games of data released, he only found four) and tried to determine the different component times of the stolen base attempt.  Some things he brought up that were interesting: “Pop” times, or the time it takes a catcher to catch the ball and get it to second base, was between 2.0 and 2.2 seconds for all attempts, which suggests that a lot of stolen bases are taken off pitchers, not catchers.  The ability to get a good lead is now measurable, as well as the jump a runner gets on the pitcher.

Jeremy also developed a model to determine the probability that a player makes a play on a ball hit near him.  The model was based on where the player is, where the ball would come down, and how long it would take the ball to get there.  From there, the player’s probability can change based on his jump, route, speed, and what I called “catching ability,” or the ability to actually make a play on the ball when in the vicinity.  It was shocking to see some of the plays made where players started out with low (less than 10 percent) chances of catching the ball, but by getting a good jump and running (quickly) in a straight line toward the ball, their probability would increase each 1/15 of a second.  He then showed the video of these plays and we were able to see the spectacular catches made by really good outfielders.  This also applies to outfielders who start with a low probability to make the catch, but increase it as they, for example, chase a ball into the gap, close quickly on it, but don’t catch it.  The ability to increase the probability of a catch is very valuable and that knowledge would be immensely valuable to teams.  Lastly, he also showed how bad outfielders can turn outs into hits by reading the ball poorly, getting bad jumps, and being indecisive.  Super cool, and as soon as the presentations are made available online (which hopefully will be soon), I will link to some of them, but especially some of these graphs.

Unfortunately, I missed the following presentations, so I will just show the abstracts presented in the program.

Where Fielders Field: Spatial and Time Considerations (Matt Thomas): Continued application of close-range photogrammetry through high-resolution digital photography to baseball is revealing hitherto unseen patterns of fielding in the game. Matt examines these patterns and where data permit, factors time into this examination. After reviewing general trends he notes specific achievements and then speculates on whether any of this freshly quantified insight tells us what makes for good (and not so good) fielding.

Scoutf/x (Max Marchi): This presentation evaluates players’ tools with Pitchf/x, Hitf/x, and Fieldf/x.

True Defensive Range (TDR): Getting out of the Zone (Greg Rybarczyk): Greg intends to display detailed tracking of the 25 batted balls in the released data that were hit in the air to the outfield. Presented data will include the relative positions of the outfielders and the ball from the time the ball leaves the bat until the time it is retrieved by the fielder. Using the essential elements of this data (fielder starting position, ball hang time and landing point), he outlines the fundamentals of a new outfield defensive metric, called ‘True Defensive Range’ or TDR, which should provide more accurate player defensive ratings with a smaller required sample size than current metrics. Full realization of this metric will require establishment of baseline values using the full data set. TDR for infielders will employ a similar method, but it will not be covered during this presentation.

The Future of Sportvision’s Data Collection (Greg Moore): Greg will talk about several bits of baseball data that Sportvision might collect in the future, and he will discuss how the data can be used in conjunction with Pitchf/x, Hitf/x, and Fieldf/x. Greg will also conclude the 2010 Pitchf/x Summit with closing remarks.

Obviously, there was a lot of cool stuff presented.  As mentioned, only 13 games worth of data were released to the analysts and most of the presentations were about determining what could be done with the data.  But with enough work and research, it will not only change the way teams and analysts evaluate players, but also will give teams another tool with which to teach their players and improve the guys they already have on the roster.  We’ll also know exactly what skills are important in each aspect of the game (base running, fielding, etc.), and as we learn these things we’ll discover other things we want to know.  I’d love to know what you guys think of all this and I’ll try to answer any questions you have about what can and can’t be measured and how we’ll use it in the future.

UPDATE: After I wrote this mess, I discovered this, much cleaner, detailed, mess, written by Baseball Prospectus writer Ben Lindbergh.  I’ll link to it down here because I want you to read what I wrote instead of Ben’s running diary.  Sorry, Ben.

This article was originally published at Knuckleballs, written by Dan Hennessey.

Matt Kemp has been struggling this season, and even if you account for the low BABIP (.303) compared to his xBABIP (.335), he is still striking out at a higher rate this season (28.1%) compared to last (22.9%). What has also confounded me is thatm even though he has a higher strikeout rate, he is also setting a career high in walk rate as well (8.1%). Usually, drawing walks and getting struck out are thought of as tradeoffs, opposite ends of the “patience scale.” Last season, Kemp hit .362/.429/.616 against LHP and .278/.329/.453 against RHP, but this season, he is down to .303/.341/.443 against LHP and .240/.306/.445 against RHP. What happened?

There are two questions I’d like to investigate: 1) Is Kemp swinging at more strikes in 2010 compared to 2009 and how? and 2) Is Kemp making less contact in 2010 compared to 2009 and how?

To answer these two questions, I’d like to look at Kemp’s swinging strike percentages (swinging strikes per pitch) and contact percentages (contact made per pitch) against all fastballs (four-seamers, two-seamers, cutters, and splitters). Checking to see any differences between 2009 and 2010 should lend some insight into Kemp’s offensive struggles this season.

I ran several regressions to model surfaces of Kemp’s swinging strike percentages and contact percentages as well as his swing zones. First up, let’s take a look at Matt Kemp’s SwStr% against RHP fastballs:

The red contour lines tell us that Kemp chooses to swing 50% of the time when a ball is thrown within the contour line. This is what I call Kemp’s swing zone, so the red circles refer to this. Further examples and explanations of these swing zones can be seen here. What the swing zones tell us here is that Kemp is swinging less at RHP fastballs in 2010, but is whiffing at a much higher rate as well. He is also missing more RHP fastballs down the middle as compared to before.

Let’s check Kemp’s Contact% against RHP fastballs to see if his swinging strikes are affecting his ability to make contact:

The red contour lines are the same as in the previous two graphs. Clearly, Kemp is making a lot less contact off RHP fastballs, and this tells me that he is putting the ball into play less. The previous two show Kemp swinging and missing more, while these two show Kemp making less contact, particularly on high inside fastballs. Let’s take a look at how Kemp has been doing against LHP fastballs, first at his SwStr%:

Here are his swinging strike plots, Kemp has actually started to swing more on LHP fastballs down and out of the zone (the red contour lines dip in 2010), so his swinging strike rate there is up. But he is also missing a lot more LHP fastballs this year that come down the middle over the plate, ideal pitches for the right-hander to hit out of the park. This is particularly concerning when you consider that Kemp’s wFB/C (runs above average per 100 fastballs) was at 1.64 last season, while that number is down to 0.38 this season. A major part of that drop must have to do with Kemp whiffing on fastballs down the middle that he used to hit.

Finally, let’s look at Kemp’s Contact% against LHP fastballs:

Looking at his contact plots, we see similar colors in where he makes the most contact (making contact 80% of pitches in those areas). But we notice a huge shift in where the epicenter of that hotspot is. Last year, Kemp made contact off a lot of LHP fastballs down the middle of the plate, but this year, the epicenter of that contact hotspot has shifted a full foot up from the direct middle of the zone to the top of the zone. We can infer that Kemp is making less contact off the sweet spot of his bat, and making more high fastball contact that usually result in pop outs. This is problematic and adds further evidence that Kemp is simply missing fastballs down the middle as well as chasing high fastballs.

In general, what I present here is what Dodgers’ fans already know: Kemp is swinging and missing a lot. But I hope that I was able to demonstrate clearly how Kemp has been struggling against fastballs, showing where he is whiffing on them and where he is making less contact.

An article over at Memories Of Kevin Malone convinced me that perhaps Kemp’s whiffing behavior this season (along with swinging less and drawing more walks) could have been caused by Kemp’s change in swinging mechanics. Finally, if you visit my blog at Think Blue Crew, you can read a longer post about Kemp’s offensive struggles against breaking balls as well.

A variation of this article was originally posted at Think Blue Crew, a blog dedicated to data visualization of baseball, basketball, and football statistics. Check it out for more f/x visualizations like this.