Home Cooking: Fouls and Home Court in the NBA

After watching Game 3 of the NBA Finals, I could not help but feel that the Miami Heat seem to receive a lot of home cooking when it comes to fouls.  I recalled that in games played in Miami  during the Eastern Conference Finals that there were several no-calls or instances where a Boston player would be bulldozed by LeBron James during a block…but a foul was not called.

During Game 3, the primary situation that many have pointed to was the push off and shoulder block by James on Oklahoma City Thunder guard James Harden that occurred near midcourt late in the game.  A foul was called on Harden despite the push off from Mr. “I don’t need an advantage.”  Some even noted that it was a reflection of James’s megastar status.

While watching that game, I came to two conclusions about how I felt about watching the Miami Heat.  First, I feel like James will score every time he touches the ball.  Even though that is not true (though he does shoot close to 50 percent for his career [48.3]), whenever the Thunder missed a shot and the Heat came down court, if LeBron had the ball, then I had a feeling that he would score.  I feel that way about two other players — Dirk Nowitzki and Kobe Bryant.

Second, if the Heat are in Miami, then LeBron James and Dwyane Wade are going to get away with more and have more fouls drawn from other players.

However, upon further review, that is not necessarily true.  Though fouls drawn is not an easy stat to obtain (and I am too lazy to put the database together), past research on this does tend to place James and Wade high on such a list (Weak Side Awareness has painstakingly compile such a list for the 2010-11 season, as well as previous seasons.  Another website also breaks this down, though for the 2006-07 season…the pattern is still the same).  However, there is no breakdown for home versus road.

Nevertheless, if one looks at personal fouls committed by the two Heat players, we see that while Wade does commit more on the road this season, James actually commits slightly more at home (1.6 at home versus 1.5 on the road).

Despite this, just because Wade and James may not be committing a lot of fouls does not mean that teams that play in Miami do not get mistreated.  We all hold that there is some homecooking when it comes to officiating.  Indeed, authors Tobias Moskowski and L. Jon Wortheim do a great job digging deep into this phenomenon in their book Scorecasting.

So, I decided to look at see not only the difference between home fouls versus road fouls for various teams, but also which venues tend to have more fouls called on opponents.  Do we see a trend that Miami is a foul party for visiting teams?

First, if we look simply at average fouls committed on the road versus those committed at home, the greatest differential is actually the Milwaukee Bucks (20.8 on the road versus 17.8 at home), followed by the Utah Jazz and then the Miami Heat, Boston Celtics, and rounding out the top five is Sacramento.  There are teams with a negative differential (Charlotte, Indiana, New York, San Antonio, and Washington).

Fouls Committed

TEAM ROAD HOME DIFF
ATL

17.8788

17.8182

0.06061

BOS

20.8788

18.9697

1.90909

CHA

18.3636

19.4848

-1.1212

CHI

17.3939

17.1212

0.27273

CLE

20.8485

19.0606

1.78788

DAL

18.8788

18.2727

0.60606

DEN

19.7576

19.5455

0.21212

DET

19.7879

19.3939

0.39394

GSW

21.6061

21.1212

0.48485

HOU

21.0303

19.7879

1.24242

IND

21.3939

21.9394

-0.5455

LAC

21.3636

21.0303

0.33333

LAL

16.9091

16.697

0.21212

MEM

20.4242

19.4545

0.9697

MIA

20.3939

18.4545

1.93939

MIL

20.7879

17.7879

3

MIN

19.0303

17.697

1.33333

NJN

19.9394

18.4848

1.45455

NOH

20.6364

19.4848

1.15152

NYK

21.0303

21.1212

-0.0909

OKC

21.1212

19.8485

1.27273

SAS

16.8182

17.8182

-1

ORL

18.1818

17.2121

0.9697

PHI

18.3636

16.697

1.66667

PHO

19.5758

17.7879

1.78788

POR

19.0606

18.8485

0.21212

SAC

20.4242

18.5758

1.84848

TOR

23.8485

22.5758

1.27273

UTA

23.0303

20.6364

2.39394

WAS

21.1515

21.4242

-0.2727

However, this does not tell the entire story.  What about when teams play at a particular venue?  While the Toronto Raptors might commit the most fouls on the road (coincidentally, they also commit the most at home), this does not tell us where they tend to commit more fouls.  So, I organized fouls committed by venue.

If your favorite team is playing the New York Knicks, expect fouls as Madison Square Garden patrons witness the most fouls by opponents (average 22.67 fouls per game).  MSG is followed by the Staples Center (but only when the Los Angeles Clippers are playing), the Pepsi Center in Denver, Bankers Life Fieldhouse in Indianapolis, and EnergySolutions Center in Salt Lake City.  American Airlines Arena — home to the Miami Heat — is eighth.  The Wells Fargo Center in Philadelphia has the lowest number of fouls by an opponent (16.5 fouls per game by opponent).

Now, using those numbers, we can determine the difference between fouls committed by opponents relative to the home team.  Number one of that list is the Los Angeles Lakers, with an average difference of 4.1 fouls [It should be noted that the Lakers commit the fewest fouls per game in the NBA].  The second greatest home foul differential belongs to the Minnesota Timberwolves, followed by the Orlando Magic, Denver Nuggets, and the Miami Heat (2.5 fouls).  Not surprisingly, the team with the worst differential is Toronto, which leads the Association in fouls per game.

FOULS

Team

Home

Opp.

Diff.

ATL

17.818

20.182

2.3636

BOS

18.97

18.03

-0.9394

CHA

19.485

21.576

2.0909

CHI

17.121

17.939

0.8182

CLE

19.061

20.848

1.7879

DAL

18.273

19.455

1.1818

DEN

19.545

22.424

2.8788

DET

19.394

19.97

0.5758

GSW

21.121

16.939

-4.1818

HOU

19.788

18.545

-1.2424

IND

21.939

22.273

0.3333

LAC

21.03

22.515

1.4848

LAL

16.697

20.788

4.0909

MEM

19.455

20.606

1.1515

MIA

18.455

20.97

2.5152

MIL

17.788

19.152

1.3636

MIN

17.697

21.636

3.9394

NJN

18.485

19.242

0.7576

NOH

19.485

18.485

-1

NYK

21.121

22.667

1.5455

OKC

19.848

19.848

0

SAS

17.818

19.818

2

ORL

17.212

20.182

2.9697

PHI

16.697

16.485

-0.2121

PHO

17.788

19.424

1.6364

POR

18.848

20.515

1.6667

SAC

18.576

20.727

2.1515

TOR

22.576

17.818

-4.7576

UTA

20.636

21.697

1.0606

WAS

21.424

19.152

-2.2727

Of course, this leads to discussion about free throws.  To simplify this, I want to look just at free throws by the home team versus road team free throws at that venue.  First, the team with the highest average of home free throws attempted is the Jazz (27.2), followed by the Nuggets, Pacers, Knicks, and Thunder [Miami is seventh].

The venue with the fewest free throws attempted by opponent is at the Staples Center, but when the Lakers play there.  This is followed by the Amway Center (Magic), United Center (Chicago Bulls), Target Center (T’wolves), and the US Airways Center (Phoenix Suns).  For the record, Miami is eighth here.

Now, by comparing the differential, we can see if a team has a scoring advantage via fouls.   Number one is Los Angeles Lakers (7.96 difference in free throws), followed by the Nuggets (6.5), the Timberwolves (5.2), the Kings (4.8), and the Heat (4.5).  Nine teams have a negative differential, mostly teams with losing records with the exception of the Celtics (-1.8) and the 76ers (-2.1).

Team

HOME

OPP

Diff

ATL

23.0303

20.6364

2.39394

BOS

19.3333

21.0909

-1.7576

CHA

24.3939

23.7576

0.63636

CHI

22.0606

19.4545

2.60606

CLE

23.9394

20.7576

3.18182

DAL

21.9394

21.3333

0.60606

DEN

27.0606

20.5455

6.51515

DET

23.0909

22.8788

0.21212

GSW

18.8182

26.8788

-8.0606

HOU

20.4848

21.4242

-0.9394

IND

26.6667

25.0606

1.60606

LAC

24.697

25.7576

-1.0606

LAL

24.6667

16.697

7.9697

MEM

25.2727

22.5455

2.72727

MIA

24.9697

20.4242

4.54545

MIL

20.3636

21.6061

-1.2424

MIN

24.8485

19.6364

5.21212

NJN

22

21.6061

0.39394

NOH

20.6061

21.6061

-1

NYK

26.0606

24.4848

1.57576

OKC

26

22.9091

3.09091

SAS

22.7576

19.7576

3

ORL

22.7273

19.1515

3.57576

PHI

17.9091

20.0303

-2.1212

PHO

21.4545

19.6667

1.78788

POR

22.6364

21.4848

1.15152

SAC

24.5455

19.697

4.84848

TOR

21.4242

26.5152

-5.0909

UTA

27.2424

23.0606

4.18182

WAS

21.3333

24.8485

-3.5152

These numbers, of course, affect scoring chances.  For example, the Lakers receive 263 more free throw attempts at home compared to their visiting opponents.  For Miami, the Heat receive 150 more attempts.

What does all of this represent?  Well, there is a noticeable discrepancy between fouls called against the Heat compared to Heat opponents in Miami, they are not the top beneficiary.  The Heat in general do not produce a lot of fouls (middle of the pack overall), which one could argue comes from the privilege of having star players, but it is not a reflection of an enormous home court bias.

Given that teams with losing records also populate the top team in terms of home foul differential (e.g. the Kings, Bobcats, and Timberwolves), it may come down to something more than home court, or even “good” or popular teams.  Remember, the top site for opposing team fouls is MSG, a mediocre team; but the Knicks are also one of the top five teams in terms of fouls committed at home.

Therefore, foul discrepancy is likely more of a reflection of style of play.  Teams that attack the basket more might be able to draw more fouls, while defensively aggressive teams may commit more fouls.

So, maybe I can lay off the Heat for a bit.  Though, I still am not going to watch a game played in Miami.

Restructured Uncle Popov Top 23 Coming Soon!

I gave it lots of thought; polls are meaningless.

But then again, so is the entire structure that is in place to determine the “national champion” in the top-tier of college football.    They might as well hold a lottery and just pick two teams from a hat.

Polls are meaningless because it is all based on perception, something covered here at U.P. ad nauseam, based predominantly on which team is on television the most.  In turn, the teams that are on television the most — at least in prime spots [not Tuesday nights] — are so-called power conference teams.  Thus, you are going to have an abundance of big name teams at the top at the expense of strong, smaller-name teams.  A recent table showing the over/undervaluation of teams backs up this point.  And while teams like Boise State and TCU have begun to crack into the upper echelon, think of how long it took for that to happen.  And, to be fair, this occurs in college basketball as well [see Gonzaga and Butler].

Now, over the past two years, we have maintained our own poll for college football.  In 2009, we had actual pollsters.  It was fun, but I could not get people to do it on a consistent basis [and the sampling was quite small].  So, in 2010, we ran a formula based on several factors, tweaking it as the season progressed.  It worked well, but there still seemed to be something missing.

Consider the following: pre-season polls are as much a reflection of recent history (i.e. last season’s performance as well as the past couple of seasons) as much as it is the potential for the upcoming season.  Remember Michigan in 2007 or Alabama in 2000 — teams ranked in the top five in pre-season polls that ended up with dismal records.  So, it is all a crapshoot to reshuffle the polls and guess which team should be number one.

The Harris Poll has the right idea by waiting a few weeks into the season before releasing its first poll.  However, the fallacy there is that there still appears to be a tendency to follow the herd of other polls.

Since the “national champion” is just made up, and that polls reflect past seasons, why not create a ranking system that is a continuous process?  Or, to put it another way, why not have a poll that directly takes into account recent history, as well as the current season?  I mean, since choosing the national champion in the FBS is no different than choosing Homer to drive the Monorail, why not do away with the poll system and have a more flexible system.

Therefore, the new U.P. Top 23 will do just that.  To hell with this arbitrary national championship.  The poll seen here will take into account recent history, as well as the present season.  It will weight conferences and recruiting and stats and bowl games evenly.  No manipulation or perception.

Think about this for a moment.  Because there is no playoff at the top-tier of college football, determining a “champion” is similar to tennis or golf.  Tiger Woods was the world’s number one player for years before his recent decline.  Similiarly, Roger Federer was the top dog in tennis year after year.  The rankings did not start over every year; it was continuous.

Thus, the new Uncle Popov Top 23 will take into account the last three years of play.  I chose three years over five years because a three-year span covers a full four years of a recruiting class [previous three years plus the current season].  Plus, it is not as cumbersome as five years.

The following measures are used in the formula:

  • Winning Percentage

It makes sense that winning is everything.  Thus, winning percentage is the heaviest weight.  The overriding portion here is winning percentage over the last three seasons, plus the current season’s winning percentage.  In other words, by the end of the 2011 season, there will be four seasons worth of data to determine the number one team.  After this season, the 2008 season will be dropped.  However, the 2011 winning percentage will not become part of the equation until the first week in October [in order to give it time to balance out].  However, wins in 2011 will be added to the overall winning percentage beginning in week one.

There is also a balance of each of the three seasons’ winning percentage.  While all three seasons are included (for this season, 2008, 2009 and 2010), more weight is given to the most recent season.  I determined that it was best to be more “what have you done for me lately” so that an isolated case of success in 2008 [e.g. Buffalo] does not overrate a team.

Additionally, a strength of schedule measure was implemented so that teams that feast on weak opponents do not become overrated as well.  Part of this was used to remove FCS schools.

  • Conference Measure

A second measure is based on the conference.  I wanted to be able to isolate conference power so that teams in “tougher conference” can carry some of that weight.  BUT, in the process, I also placed a measure for out-of-conference scheduling.  So, while the SEC may have stiff competition within the conference, the same cannot be said about its OOC scheduling.

Again, the previous three seasons will be incorporated here.  A team’s winning percentage within the conference will be measured against a conference weight; the same will be done for out-of-conference winning percentages.  For example, the SEC had the highest conference weight while the Sun Belt had the lowest.  So, even though Troy has a higher conference winning percentage than Georgia, Georgia’s conference weight is higher  than Troy.  Same thing applies to the OOC weight, where Air Force has a higher OOC weight than Alabama, despite the latter going undefeated over the past three seasons.

  • Bowl Games

I felt it was necessary to separate the bowl games from the regular season.  I opted NOT to separate out conference championship games since not every conference possesses one.

With the bowl games, teams are awarded a “bonus” for simply making it to a bowl game.  There is weight giving to winning a bowl game, but losing a bowl game with give a time points as well [think of how the NHL awards points for overtime losses].  I decided against weighing “prestigious” bowl games because sometimes teams will not bother putting much effort into a bowl game [see Alabama v. Utah in the 2009 Sugar Bowl].  Thus, all bowl games — including the arbitrary BCS Championship Game — are initially weighed evenly.

The variance comes from a conference bowl weight.  With this, a conferences’ winning percentage is taken into account.  Thus, because the SEC does well in bowl games, they will get a boost while the Big Ten gets hammered.  Hey, if conferences can be rewarded with multiple bowl games, then they should also be punished for consistently losing them!

  • Recruiting

I nearly opted NOT to use this as a measure.  Recruiting and the “star-system” is no different than pre-season polls — guessing!  But, it is a glimpse into the future.  So, even if it is the lowest weight in the formula, it was worth while to include it.

For this measure, I opted to use Rivals.com’s rating system.  Also, instead of simply measuring all 120 teams against one another, I measured each team against its conference counterparts.  For a multitude of reasons, it is unfair to measure Alabama and Texas against Boise State and TCU.  Thus, it is best to measure Alabama against SEC teams and Boise State against WAC and now Mountain West teams.

The method I employed will not punish a team like Texas because of their high number of top-notch recruits — the conference weight will still push them up — but it brings teams like TCU to a much more level playing field when comparing the two; like examining per capita GDP rather than total GDP.

For my sanity, the recruiting measure will only be updated once a month.

  • Team Statistics

Finally, team statistics are brought into play, just as it was last year.  It is not straight stats, but the offensive and defensive stats are measured against the stats of the opponents.  Thus, while a team like Boise State may have dominant offensive stats, it comes against comparatively inferior defenses.  Ergo, it is adjusted accordingly.

This year, I expanded the statistics to include measures for passing and rushing rather than relying solely on total stats.

Also, just like last year, a measure is included for point differential.

  • THE RATING!!!

All of this is plugged into my “secret formula.”  As I noted last year, I will anally protect my formula and not publicly release just like those other computer polls.  There is some fun in the secrecy, anyway.  It is actually a two-step process.

With all of the above, an initial rating is produce.  That rating is then plugged back into a team’s schedule to determine quality wins and quality losses.  Again, FCS teams are removed from the wins.  However, a loss to an FCS team does carry weight…a lot of negative weight!  After this, a new formula is run and the final ranking is produced.

All of this stated, no one really gives a shit about our poll!  But, at least I explained from where I am coming.  The first U.P. Top 23 will be released next week and will actually include all 120 teams!

Chasing a Dream: Historical Stats for the 2011 MLB All-Stars at Chase Field

This blog entry derived out of search entries that directed some people to ‘Uncle Popov.’  Those curious folks were interested in batting averages at Chase Field, home to tonight’s 2011 MLB All-Star Game.  So, thanks curious cats!

It is difficult to get excited about a game that is an exhibition, no matter how many times Major League Baseball (and their advertisers) tells us that “this one counts.”  No, it is still an exhibition game.

What really counts for MLB is that a lot of people will watch the overhyped exhibition game.  And while it is an exhibition, many people are interested in seeing how batters and pitchers that do not normal face each other will do once the game begins, such as Roy Halladay versus Jose Bautista.

Playing in a different park is also worth noting.  The All-Star Game gives MLB a chance to showcase its stadiums; sometimes it is a well-known stadium (as in 2008 at [old] Yankee Stadium) and sometimes it is not that well-known, as in this year’s game at Chase Field in Arizona.

Chase Field has been the home of the Arizona Diamondbacks since its inception in 1998.  It has hosted college football games, a World Series, and now will host its first All-Star Game.  Given that it has been around for over 13 seasons, most players — including many of the All-Stars in tonight’s game — have played at Chase Field.

So, how have they done at the stadium formerly known as BankOne Ballpark?  Well, a few things to keep in mind as there are several factors in determining how often a player actually plays in Phoenix.  First and most obvious, members of the Diamondbacks have the most appearances.  This is typically followed by other NL West teams, then other National League teams, any American League players that have played either with Arizona or in the NL (see Jose Valverde and Carlos Quentin, for example), and lastly the rest of the American League.

In other words, the sampling is going to be varied.  Nevertheless…

  • NATIONAL LEAGUE ALL-STARS

Hunter Pence has the highest batting average of all NL players, going 0.364 in 55 career at-bats, with three homers and 11 RBIs.  He is also 2-for-4 in stolen bases and has struck out 12 times.  Of the non-Diamondback players, St. Louis’s Lance Berkman has the most home runs with 13 (far behind D’back Justin Upton’s 47), as well as the most RBIs (38).  Los Angeles Dodger Andre Ethier has the most at-bats for a non-D’back (168), while Berkman is second among players not in the NL West (139).

The worst batting average at Chase Field for an NL player is Andrew McCutchen (0.200 in 30 at-bats) and starting shortstop Troy Tulowitzki (0.200 in 130 at-bats).  Only one player has never hit a home run at Chase (Chicago Cubs phenom Starlin Castro).  And in terms of the two hometown players, Justin Upton ranks eighth in Chase Field batting average (0.305), while catcher Miguel Montero is batting 0.271, good for 15th out of the 21 position players.

Here is a look at the stats for the starting lineup for the National League (in batting order):

  1. Rickie Weeks (Brewers, 2B) – 0.345; two HRs; five RBIs in 55 at-bats
  2. Carlos Beltran (Mets, DH) – 0.286; seven HRs; 18 RBIs in 70 at-bats
  3. Matt Kemp (Dodgers, CF) – 0.306; five HRs; 14 RBIs in 124 at-bats
  4. Prince Fielder (Brewers, 1B) – 0.288; four HRs; ten RBIs in 66 at-bats
  5. Brian McCann (Braves, C) – 0.265; one HR; 13 RBIs in 68 at-bats
  6. Lance Berkman (Cardinals, RF) – 0.302; 13 HRs; 38 RBIs in 139 at-bats
  7. Matt Holliday (Cardinals, LF) – 0.281; four HRs; 22 RBIs in 167 at-bats
  8. Troy Tulowitzki (Rockies, SS) – 0.200; five HRs; 14 RBIs in 130 at-bats
  9. Scott Rolen (Reds, 3B) – 0.256; six HRs; 23 RBIs in 125 at-bats

In terms of pitchers, there are no Diamondback pitchers on the roster.  Nevertheless, in terms of ERA, Atlanta Braves reliever Jonny Venters tops everyone with 0.00 ERA in three games.  However, he is only 1-0 in those three games, blowing two saves and allowing three unearned runs in his four innings at Chase Field.  He is followed by San Francisco Giants closer Brian Wilson, who is posting a 1.62 ERA in 16.2 innings with 15 saves (only one blown save), 21 strikeouts and an NL All-Star best 0.167 batting average against.

The best starter has been Ryan Vogelsong, who is 2-1 in Phoenix with a 1.69 ERA and a 1.19 WHIP in 16 innings.  The NL starter Roy Halladay only has one start at Chase Field, getting the win going six innings while giving up three runs (4.50 ERA) and striking out four.  Tim Lincecum has the most wins (3), Kevin Correia has the most losses (6) and strikeouts (48), and Heath Bell has the most holds (4).

The worst pitcher at Chase Field is Atlanta closer Craig Kimbrel (13.50 ERA with one loss and one blown save), followed by Cliff Lee (5.14 ERA in seven innings) and Jair Jurrjens (5.09 ERA in three games).

  • AMERICAN LEAGUE ALL-STARS

The American League hitters provide fewer numbers.  In fact, five players — Jacoby Ellsbury, Howie Kendrick, Jhonny Peralta, Matt Wieters, and starting catcher Alex Avila have never played at Chase Field.

Nevertheless, among those that have walked out onto Chase Field, Josh Hamilton has the best batting average, hitting 0.429 with two home runs and three RBIs in seven at-bats (three games).  Final vote-winner Paul Konerko is second with a 0.409 batting average.  In 22 at-bats over nine games, Konerko has five home runs and seven RBIs, scoring seven runs himself.  Third is Miguel Cabrera (0.393 batting average).

The AL player with the most at bats at Chase Field is former D’back Quentin, who in 209 at-bats is batting 0.287 with eight homers and 44 RBIs (the latter is the most for any AL player).  Starting third-basemen Adrian Beltre has the most at-bats (193) for any AL player never affiliated with Arizona, going 0.275 with ten home runs and 29 RBIs.

The worst batting average at Chase is Matt Joyce, who in six at-bats is batting 0.167.  He does have one home run, but that is his only hit.  He is followed by Michael Cuddyer (0.190 in 21 at-bats) and Kevin Youkilis (0.222 in nine at-bats). Robinson Cano has the fourth-worst batting average in Phoenix, going 0.250.  Ironically, the 2011 Home Run Derby champion also has zero career home runs at Chase Field.  He joins Youkilis, Michael Young and David Ortiz as the only AL-players to not hit a home run at Chase Field.

Here are the stats for the AL-starters, again in batting order:

  1. Curtis Granderson (Yankees, CF) – 0.273; one HR; three RBIs in 22 at-bats
  2. Asdrubal Cabrera (Indians, SS) – 0.385; one HR; three RBIs in 13 at-bats
  3. Adrian Gonzalez (Red Sox, 1B) – 0.314; 13 HRs; 37 RBIs in 169 at-bats
  4. Jose Bautista (Blue Jays, RF) – 0.265; two HRs; six RBIs in 34 at-bats
  5. Josh Hamilton (Rangers, LF) – 0.429; two HRs; three RBIs in seven at-bats
  6. Adrian Beltre (Rangers, 3B) – 0.275; ten HRs; 29 RBIs in 193 at-bats
  7. David Ortiz (Red Sox, DH) – 0.286; zero HRs; zero RBIs in seven at-bats
  8. Robinson Cano (Yankees, 2B) – 0.250; zero HRs; one RBI in 12 at-bats
  9. Alex Avila (Tigers, C) – never played at Chase Field

Pitching stats are scarce for the AL pitchers, as only six — Josh Beckett, Jered Weaver, David Robertson, Jose Valverde, Chris Perez, and C.J. Wilson — have actually pitched at Chase Field.  Three of those six players (Beckett, Valverde, and Perez) pitched in the National League at one time.

Obviously former Diamondback Valverde will have the most games played (138), most wins (seven), most losses (nine), and most saves (51).  Perez and Wilson are the only AL pitchers with a save (one) at Chase Field, while Valverde [during his days as a set-up man] is the only pitcher with any holds (nine).

Wilson has the best ERA (0.00), which was accomplished in 2.1 innings while allowing two hits.  Beckett has four career starts at Chase Field, going 1-2 in those games while accumulating an AL-worst 5.16 ERA.  The AL starter Jered Weaver has only one appearance, going six innings and giving up three runs (4.50 ERA) on four hits (0.182 batting average against) while picking up the win.

So, there you have it!  The stats for the players in the 2011 MLB All-Star Game.  Happy now?

First-Pitch Swinging: Best First-Pitch Hitters in MLB (2010)

It is a bit delayed as I meant to post this before the season got underway.  But, better late than never.

Continuing the theme of odd-statistics in the stat-driven sport of baseball, I decided to look at the best hitters of 0-0 counts.  When one thinks of current players that are swinging away at first pitches, one person that tends to jump out if Vlad Guerrero.  While he is top ten, he is not the best first-pitch hitter in baseball…at least in 2010.  So let’s take a look and found out which players waste little time getting to work.

The stats used again come from ESPN.com, which does a good job of providing a ton of situational stats by teams, thereby making it easier to acquire and sort.  Like the Uncle Popov Top 23 for college football, I made up my own formula and it is probably flawed.  But, it gave me a method to decipher who was the best hitter on an 0-0 count.  Similar to how I created a “qualified” category for the road-home splits, I took the top ten players by at-bats (for an 0-0 count) per team and weeded out the remainder.  Then, I grouped each of the First-Pitch qualifiers by league -AL and NL.

From here, I did two rankings on a number of statistical categories, including hits, runs, HRs, RBIs, batting average, OPS, etc.  The first ranking is the player’s statistical rank among all of his AL or NL peers (depending on the league).  The second ranking is the player’s statistical rank among his (qualified) teammates.  For example, Ichiro Suzuki ranks 15th in the AL for first-pitch doubles, but first in that category among the Seattle Mariners.

Once I had the ranks for each, I average each ranking (league and team) and then added the two numbers to get a “ranking score.”  The lower the ranking, the better the first-pitch hitter.  Well, roughly.

One thing to keep in mind — the stats collecting are from when players put the 0-0 pitch in play.  Obviously every batter faces an 0-0 pitch.  However, statistics are calculated based on that pitch being put into play.  As you will see below, the most at-bats where a first-pitch was put into play comes from Vernon Wells, who in total had 590 at-bats; this means that he had an 0-0 count 590 times.  However, of those 590 first pitches Wells put 133 into play (22.5 percent of the time).  The statistics seen here come from the times when the ball is put into play, which in the case of Wells was 22.5 percent of the time.  Make sense?  If not, read it again!

Now, here are the best first-pitch hitters by league:

AMERICAN LEAGUE

5. Miguel Cabrera (Detroit Tigers) – 19.7

4. Delmon Young (Minnesota Twins) – 19.5

3. Justin Morneau (Minnesota Twins) – 16.0

2. Carl Crawford (Tampa Bay Rays) – 14.8

1. Nelson Cruz (Texas Rangers) – 9.6

  • It makes sense that a Ranger is tops here as Texas had the best average rank (3.125) of any team in the Majors, with three Rangers in the top ten (the other two are Vladimir Guerrero and Josh Hamilton).  In other words, they are free-swinging in DFW!  Cruz batted 0.484 with six homers, 20 RBIs, nine doubles and even one triple.  He put the first pitch into play 15.5 percent of the time and 27.7 percent of his home runs occurred on the first pitch; he hit more home runs on the first pitch than any other count.  Swing away, Cruz; swing away.

NATIONAL LEAGUE

5. Geovany Soto (Chicago Cubs) – 20.5

4. Kelly Johnson (Arizona Diamondbacks) – 19.8

3. Rickie Weeks (Milwaukee Brewers) – 17.3

2. Corey Hart (Milwaukee Brewers) – 16.5

1. Colby Rasmus (St. Louis Cardinals) – 11.2

  • Rasmus batted a crazy 0.477 on the first pitch, which ranked 11th among National League batters.  He also jacked eight home runs and drove in 19 runs off the 0-0 pitch, the former being tied for second in the National League.  Rasmus put 14 percent of first pitches into play.  His eight first-pitch home runs accounted for 34.8 percent of all of his home runs (like Cruz, a plurality of his home runs occurred on the first pitch).  All of this despite the fact that the Cardinals are middle-of-the-pack in the NL in terms of 0-0 pitches.

And now, for some other stats related to first-pitch situations.

MOST FIRST-PITCH AT-BATS (putting ball in play):

  • American League: Vernon Wells (TOR) – 133 at-bats
  • National League: Carlos Lee (HOU) and Pablo Sandoval (SF) – 100 at-bats

MOST FIRST-PITCH DOUBLES:

  • American League: Miguel Cabrera (DET) – 12 doubles
  • National League: Marlon Byrd (CHC) – 13 doubles

MOST FIRST-PITCH TRIPLES:

  • American League: Carl Crawford (TB) – four triples
  • National League: Dexter Fowler (COL) – four triples

MOST FIRST-PITCH HOME RUNS:

  • American League: Vladimir Guerrero (TEX) – 10 home runs
  • National League: Carlos Gonzalez (COL) – 9 home runs

MOST FIRST-PITCH RBIs:

  • American League: Vladimir Guerrero (TEX) – 39 RBIs
  • National League: Corey Hart (MIL) – 28 RBIs

BEST FIRST-PITCH BATTING AVERAGE:

  • American League: Jim Thome (MIN) – 0.577
  • National League: Kelly Johnson (ARZ) – 0.551

BEST FIRST-PITCH SLUGGING PERCENTAGE:

  • American League: Jim Thome (MIN) – 1.500
  • National League: Geovany Soto (CHC) – 1.156

FIRST-PITCH TEAM BATTING AVERAGES (American League):

  1. Minnesota Twins – 0.375
  2. Baltimore Orioles – 0.365
  3. Texas Rangers – 0.353
  4. Tampa Bay Rays – 0.344
  5. Boston Red Sox – 0.343
  6. Cleveland Indians – 0.341
  7. Oakland Athletics – 0.336
  8. Detroit Tigers – 0.334
  9. New York Yankees – 0.320
  10. Toronto Blue Jays – 0.313
  11. Kansas City Royals – 0.310
  12. Seattle Mariners – 0.305
  13. Los Angeles Angels – 0.294
  14. Chicago White Sox – 0.293

FIRST-PITCH TEAM BATTING AVERAGES (National League):

  1. Colorado Rockies – 0.385
  2. San Diego Padres – 0.366
  3. Arizona Diamondbacks – 0.360
  4. Florida Marlins – 0.354
  5. Milwaukee Brewers – 0.349
  6. New York Mets – 0.345
  7. Chicago Cubs – 0.338
  8. Pittsburgh Pirates – 0.338
  9. San Francisco Giants – 0.334
  10. Cincinnati Reds – 0.333
  11. Los Angeles Dodgers – 0.331
  12. St. Louis Cardinals – 0.320
  13. Washington Nationals – 0.320
  14. Atlanta Braves – 0.315
  15. Philadelphia Phillies – 0.308
  16. Houston Astros – 0.295

———-FILES———-

American League stats

National league stats

~~NOTE: the above files are in .docx format and may not open in older versions of Microsoft Word.  All photos taken from Daylife, with both coming via Getty Images…big ups!~~

Home Away from Home: Best Road Performances in Baseball for 2010 (MLB)

With Opening Day 2011 rapidly approaching, I decided to keep the statistical review of the 2010 season in Major League Baseball going.  Today, I want to look at the home/away splits for both leagues.  I am primarily interested in how well a player hits away from his home park.

To do this, I took the top ten players (in terms of number of at-bats) for every team for both home games and away games.  This break meant that road at-bats ranged from 344 (Ichiro Suzuki) to 94 (Jed Lowrie).  Because there was such a great disparity, I opted to then take that list and for each league and for each split stat I made a cut — top 100 at-bats (per split stat) for the AL and top 110 for NL — that put the minimum at-bats around 175 for home games and 160 for away games.

Making the cut did alter the original list somewhat.  For example, using the top 10 per team, John Jaso of the Tampa Bay Rays had the greatest positive batting average disparity between away and home games (+ 0.102).  However, when I used a “qualifying” cut-off, the leader for said stat becomes Erick Aybar of the Los Angeles Angels (+ 0.073), who would have been number four in the other list.

All stats were taken from MLB.com and then placed into an Excel spreadsheet.  I then subtracted the away numbers from the home numbers to get the disparity between the two split stats.  Any player with a positive number demonstrates a better performance on the road than at home; any player with a negative number demonstrates a poorer performance on the road than at home.

And now…the numbers.

AMERICAN LEAGUE

HIGHER AWAY BATTING AVERAGE

5. Matt LaPorta (Cleveland): +0.045

4. Mark Ellis (Oakland): +0.053

3. Juan Rivera (Los Angeles): +0.060

2. Torii Hunter (Los Angeles): +0.061

1. Erick Aybar (Los Angeles): +0.073

  • Away batting average: 0.287
  • Home batting average: 0.214
  • Best Division Park: Oakland Coliseum (0.341)
  • Best Non-Division Park: Dodgers Stadium (0.400)

MORE AWAY HOME RUNS

3t. Joe Mauer (Minnesota): +7

3t. Juan Rivera (Los Angeles): +7

3t. Torii Hunter (Los Angeles): +7

2. Daric Barton (Oakland): +8

1. Delmon Young (Minnesota): +9

  • Home runs hit on the road: 15
  • Home runs hit at home: 6
  • Best Division Park: Kauffman Stadium and U.S. Cellular Field (2)
  • Best Non-Division Park: Angel Stadium, Camden Yards, and Rogers Center (2)

MORE AWAY RBIs

5. Evan Longoria (Tampa Bay): +18

4. Mark Ellis (Oakland): +19

3. Juan Rivera (Los Angeles): +20

2. B.J. Upton (Tampa Bay): +22

1. Ben Zobrist (Tampa Bay): +33

  • Runs batted in on the road: 54
  • Runs batted in at home: 21
  • Best Division Park: Fenway Park (10)
  • Best Non-Division Park: Angel Stadium (5)

MORE AWAY WALKS DRAWN

3t. Jorge Posada (New York): +9

3t. Scott Podsednik (Kansas City): +9

3t. Torii Hunter (Los Angeles): +9

2. Rajai Davis (Oakland): +12

1. Johnny Damon (Detroit): +13

  • Walks drawn on the road: 41
  • Walks drawn at home: 28
  • Best Division Park: Progressive Field (8)
  • Best Non-Division Park: Rangers Ballpark (6)

MORE AWAY BASES STOLEN

5t. Franklin Gutierrez (Seattle): +7

5t. Brett Gardner (New York): +7

4. Johnny Damon (Detroit): +9

3. Rajai Davis (Oakland): +10

2. Carl Crawford (Tampa Bay): +13

1. Alex Rios (Chicago): +14

  • Bases stolen on the road: 24
  • Bases stolen at home: 10
  • Best Division Park: Progressive Field (4)
  • Best Non-Division Park: Rangers Ballpark (4)

———-

LOWER AWAY BATTING AVERAGE

5. Brandon Inge (Detroit): -0.082

4. Jorge Posada (New York): -0.083

3. Vernon Wells (Toronto): -0.096

2. Nelson Cruz (Texas): -0.104

1. Luke Scott (Baltimore): -0.110

FEWER AWAY HOME RUNS

5t. Michael Young (Texas): -11

5t. Vernon Wells (Toronto): -11

5t. Luke Scott (Baltimore): -11

3t. Jose Bautista (Toronto): -12

3t. Carlos Quentin (Chicago): -12

1t. Josh Hamilton (Texas): -14

1t. Paul Konerko (Chicago): -14

FEWER AWAY RBIs

5t. Mark Teixeria (New York): -20

5t. Luke Scott (Baltimore): -20

4. Kurt Suzuki (Oakland): -21

3. Vernon Wells (Toronto): -24

1t. Robinson Cano (New York): -25

1t. Miguel Tejada (Baltimore): -25

FEWER AWAY WALKS DRAWN

4t. Aaron Hill (Toronto): -13

4t. Mark Teixeria (New York): -13

3. Paul Konerko (Chicago): -14

2. Miguel Cabrera (Detroit): -15

1. Ian Kinsler (Texas): -16

FEWER AWAY BASES STOLEN

3t. Ichiro Suzuki (Seattle): -6

3t. Juan Pierre (Chicago): -6

3t. Bobby Abreu (Los Angeles): -6

2. Brennan Boesch (Detroit): -7

1. B.J. Upton (Tampa Bay): -8

NATIONAL LEAGUE

HIGHER AWAY BATTING AVERAGE

5. Carlos Ruiz (Philadelphia): +0.053

4. David Eckstein (San Diego): +0.056

3. Colby Rasmus (St. Louis): +0.058

2. Ryan Braun (Milwaukee): +0.070

1. Buster Posey (San Francisco): +0.093

  • Away batting average: 0.351
  • Home batting average: 0.258
  • Best Division Park: Chase Field (.440)
  • Best Non-Division Park: Miller Park (.600)

MORE AWAY HOME RUNS

5t. Omar Infante (Atlanta): +6

5t. Ian Stewart (Colorado): +6

5t. Buster Posey (San Francisco): +6

3t. Martin Prado (Atlanta): +7

3t. Ryan Zimmerman (Washington): +7

2. Albert Pujols (St. Louis): +8

1. Adrian Gonzalez (San Diego): +9

  • Home runs hit on the road: 20
  • Home runs hit at home: 11
  • Best Division Park: Chase Field and Coors Field (3)
  • Best Non-Division Park: Citizens Bank Park (3)

MORE AWAY RBIs

4t. Brian McCann (Atlanta): +13

4t. Juan Uribe (San Francisco): +13

4t. Matt Kemp (Los Angeles): +13

4t. Justin Upton (Arizona): +13

3. Casey Blake (Los Angeles): +14

2. Will Venable (San Diego): +15

1. Adrian Gonzalez (San Diego): +17

  • Runs batted in on the road: 59
  • Runs batted in at home: 42
  • Best Division Park: Coors Field (8)
  • Best Non-Division Park: Citizens Bank Park (6)

MORE AWAY WALKS DRAWN

5t. Jonny Gomes (Cincinnati): +9

5t. Alfonso Soriano (Chicago): +9

5t. Joey Votto (Cincinnati): +9

3t. Martin Prado (Atlanta): +10

3t. Alcides Escobar (Milwaukee): +10

2. Jimmy Rollins (Philadelphia): +12

1. Jason Heyward (Atlanta): +13

  • Walks drawn on the road: 52
  • Walks drawn at home: 39
  • Best Division Park: Sun Life Stadium (10)
  • Best Non-Division Park: PNC Park (5)

MORE AWAY BASES STOLEN

5. Five tied with +5

4. Carlos Gonzalez (Colorado): +6

3. Joey Votto (Cincinnati): +8

2. Justin Upton (Arizona): +8

1. Nyjer Morgan (Washington): +10

  • Bases stolen on the road: 22
  • Bases stolen at home: 12
  • Best Division Park: Sun Life Stadium (3)
  • Best Non-Division Park: Great American (3)

———-

LOWER AWAY BATTING AVERAGE

5. Cody Ross (Florida): -0.088

4. Carlos Gonzalez (Colorado): -0.091

3. Dexter Fowler (Colorado): -0.102

2. Miguel Olivo (Colorado): -0.107

1. Pablo Sandoval (San Francisco): -0.122

FEWER AWAY HOME RUNS

5. Jayson Werth (Philadelphia ): -9

4. Mark Reynolds (Arizona): -10

3. Jay Bruce (Cincinnati): -13

2. Chris Young (Arizona): -13

1. Carlos Gonzalez (Colorado): -18

FEWER AWAY RBIs

4t. Troy Tulowitzki (Colorado): -23

4t. Aramis Ramirez (Chicago): -23

3. Kelly Johnson (Arizona): -25

2. Miguel Olivo (Colorado): -26

1. Carlos Gonzalez (Colorado): -35

FEWER AWAY WALKS DRAWN

4t. Casey Blake (Los Angeles): -14

4t. Stephen Drew (Arizona): -14

3. Starlin Castro (Chicago): -15

2. David Wright (New York): -21

1. Matt Holliday (St. Louis): -23

FEWER AWAY BASES STOLEN

5t. Orlando Cabrera (Cincinnati): -5

5t. Matt Kemp (Los Angeles): -5

3t. Chris Young (Arizona): -6

3t. Andres Torres (San Francisco): -6

2. Angel Pagan (New York): -7

1. Albert Pujols (St. Louis): -10

Seeding Mismatches for the 2011 NCAA Tournament

Just as I did last year, I am going to examine the seeding mismatch relative to the RPI ratings.  Now, through various discussions — both here (via comments) and elsewhere — it has become understood and agreed that RPI is not the ultimate measure for tournament selection.  While true, should it not at least count towards seeding?  Even if it is the Sagarin Index, something needs to be used to place logic behind the seedings (and, I’d be willing to look at Sagarin mismatches if I have the time).

Still, while it is all too easy to point to Utah State and say that they lost and did not deserve a higher seeding, their placement as a 12 also gave them a tougher opening round matchup.

Here are the differences between RPI-predicted seeding and actual seeding.  A zero means the team was appropriately seeded; positive numbers mean the team was over-seeded (seeded higher than expected); negative numbers mean the team was under-seeded (seeded lower than expected).

Team Predicted Actual Seed Dif
Ohio State 1 1 0
Kansas 1 1 0
Pittsburgh 3 1 2
Duke 1 1 0
San Diego State 1 2 -1
Florida 2 2 0
Notre Dame 3 2 1
North Carolina 2 2 0
Syracuse 5 3 2
Purdue 3 3 0
BYU 2 3 -1
UConn 4 3 1
Texas 3 4 -1
Wisconsin 4 4 0
Louisville 5 4 1
Kentucky 2 4 -2
West Virginia 6 5 1
Vanderbilt 7 5 2
Kansas State 6 5 1
Arizona 5 5 0
Cincinnati 9 6 3
St. John’s 6 6 0
Georgetown 4 6 -2
Xavier 6 6 0
Washington 8 7 1
Texas A&M 8 7 1
UCLA 11 7 4
Temple 8 7 1
Michigan 12 8 4
Butler 9 8 1
UNLV 7 8 -1
George Mason 7 8 -1
Villanova 10 9 1
Illinois 11 9 2
Old Dominion 5 9 -4
Tennessee 9 9 0
Penn State 10 10 0
Michigan State 11 10 1
Florida State 13 10 3
Georgia 11 10 1
Marquette 13 11 2
USC 14 11 3
Gonzaga 13 11 2
Missouri 9 11 -2
Virginia Commonwealth 12 11 1
UAB 8 12 -4
Memphis 7 12 -5
Utah State 4 12 -8
Richmond 10 13 -3
Clemson 13 12 1
Princeton 10 13 -3
Morehead State 14 13 1
Belmont 12 13 -1
Oakland 12 13 -1
Bucknell 14 14 0
Wofford 15 14 1
St. Peter’s 16 14 2
Indiana State 15 14 1
Long Island University 14 15 -1
Akron 15 15 0
UC Santa Barbara 17 15 2
Northern Colorado 15 15 0
Hampton 16 16 0
UNC Asheville 17 17 0
Boston University 16 16 0
Arkansas-Little Rock 17 17 0
Texas-San Antonio 16 17 -1
Alabama State 17 17 0

Here is the breakdown by conference:

  • ACC: +1
  • Atlantic Sun: -1
  • Big 12: -0.2
  • Big East: +1.1
  • Big Sky: 0
  • Big South: 0
  • Big Ten: +1
  • Big West: +2
  • Colonial: -1.33
  • Conference USA: -4.5
  • Horizon: +1
  • Ivy League: -3
  • MAAC: +2
  • MEAC: 0
  • Mid-American: 0
  • Missouri Valley: +1
  • Mountain West: -1
  • Northeast: -1
  • Ohio Valley: +1
  • Pac-10: +1.8
  • Patriot: 0
  • SEC: +0.2
  • Southland: -1
  • Summit: -1
  • Sun Belt: 0
  • SWAC: 0
  • WAC: -8
  • West Coast: +2

~Quick Explanation: Predicted seeds are based on RPI order only of tournament teams.  Teams with an RPI 1-4 would be the top four seeds, meaning in the end the last seeds should have an RPI between 65-68.  However, because the selections have automatic qualifiers  with RPIs greater than 68, the tournament teams are ordered based off of their RPI — first is Kansas (RPI 1) and last is UNC Asheville (RPI 322).

For each set of four, there is a predicted seeding — 1-4 are predicted to receive the number 1 seeds; 5-8 are predicted to receive the number 2 seeds; 9-12 are predicted to receive the number 3 seeds; etc.  That predicted seed is subtracted from the actual seed to get the seed difference in order to determine mismatch.

For teams 65-68, those teams are given a predicted seed of 17.  Seed 17 goes to the AQ teams that participate in the “First Four” (i.e., not the last four at-large teams in).  Thus, the teams predicted to participate in the “play-in” game are UNC Asheville, UC Santa Barbara, Alabama State, and Arkansas-Little Rock.  Of those that had an actual “17-seed,” three of the four were involved in the “First Four” — only UC Santa Barbara was seeded higher, giving way to Texas-San Antonio.

Most Clutch Hitters During the First Half of the 2010 MLB Season

Okay, I am bored.  Let’s look at stats!

The All-Star Farce wrapped up last night and I watched all of one inning.  That makes two years in a row that I actually watched any part of baseball’s All-Star Game, although I still have not watched a complete game since 2002.

I am not going to bitch and moan about the game and how players are selected.  It is a beauty contest and name recognition that “excites” the fans during the doldrums of the sports calendar.  But, I would like to look at some of the best players of the first “half” of the MLB season.  In particular, I want to look at clutch players.

Anyone with a computer can go look up who is leading the league in homers or batting average (or who is turning into Richie Sexson).  But it takes someone with free time on their hands to discover who the clutch All-Stars are.  And we have done that here at Uncle Popov.  So…

BEST LATE INNING HITTERS (minimum 25 at bats)

  1. Nick Markakis (0.556 OBP; 3 RBIs; 12 BB; 10 Ks)
  2. Ichiro Suzuki (0.537 OBP; 0.457 Batting Average; 3 RBIs; 6 BB; 5 Ks)
  3. Josh Hamilton (0.500 OBP; 3 HRs; 9 RBIs; 6 BB; 6 Ks)
  4. Albert Pujols (0.500 OBP; 2 HRs; 4 RBIs; 10 BB; 7 Ks)
  5. Michael Young (0.489 OBP; 9 runs scored; 9 BB; 8 Ks)

This scenario is derived from games that are close in the late innings.  The players listed above are ranked based on on-base percentage due to the significance of drawing walks late in games, especially against tough relievers.  All are familiar names, although Nick Markakis topping the list is a surprise (0.394 batting average places him seventh).  Considered how some people criticized Ichiro for not being clutch (see comments section of link), the fact that he is second here (and first in batting average) should put that criticism to rest (it won’t, but it should).

Josh Hamilton is probably the most clutch hitter so far in 2010.  He is fourth in batting average, tied for second in home runs (Matt Holliday, Matt Kemp, Paul Konerko, and Ryan Doumit lead with four home runs each) and RBIs, and is tied with Jose Lopez for first in hits (16).

BEST WITH RUNNERS IN SCORING POSITION (minimum 50 at bats)

  1. Albert Pujols (0.530 OBP; 6 HRs; 42 RBIs; 39 runs scores; 33 BBs; 7 Ks)
  2. Adrian Gonzalez (0.527 OBP; 3 HRs; 34 RBIs; 33 runs scored; 23 BBs; 10 Ks)
  3. Ryan Ludwick (0.500 OBP; 5 HRs; 34 RBIs; 0.446 batting average; 8 BBs; 12 Ks)
  4. Chipper Jones (0.500 OBP; 2 HRs; 28 RBIs; 26 BBs; 14 Ks)
  5. Elvis Andrus (0.494 OBP; 24 RBIs; 47 runs scored; 16 BBs; 11 Ks)

Given the role of hitters batting third, fourth, fifth and sixth to drive in runs, it is not surprising to see Pujols and Gonzalez on this list; they usually have the table set for them.  However, Andrus is in at five and he is primarily the Rangers’ leadoff hitter.  Additionally, players like Carlos Quentin (0.474 OBP with RISP) are in the top 10 despite batting 0.244 on the season!  [Apparently Quentin needs runners on base as he is batting just 0.189 with the bases empty].  Quentin, along with the Reds’ Jonny Gomes, lead the league with eight home runs with RISP.

In terms of the top 5, Pujols is indeed a machine, making his second appearance in these listings.  He is dangerous enough to lead all batters with runners in scoring position in walks.  Seven strikeouts in 79 at bats is also impressive.

BEST HITTERS AFTER FALLING BEHIND IN COUNT (minimum 70 at bats)

  1. Adrian Beltre (0.325 batting average; 2 HRs; 14 RBIs; 32 Ks)
  2. Placido Polanco (0.323 batting average; 1 HR; 7 RBIs; 15 Ks)
  3. Dustin Pedroia (0.321 batting average; 4 HRs; 17 RBIs; 13 Ks)
  4. James Loney (0.314 batting average; 2 HRs; 11 RBIs; 30 Ks)
  5. Marlon Byrd (0.311 batting average; 3 HRs; 14 RBIs; 31 Ks)

This was a tough stat to grab.  MLB’s website was the source and while I would have liked to look at on-base percentage rather than batting average, for some reason the stats did not include walks.  It did give OBP but because I could not determine walks I chose to use batting average instead.  For the record, Juan Pierre (0.336) has the best OBP after falling behind, followed by Byrd (0.329), Beltre (0.328), Pedroia (0.327), and Polanco (0.323).

It is tough to say what really makes a player a good hitter after falling behind in the count.  Other than Polanco, there is nothing in these players’ career stats that suggest they are consistently good hitters after falling behind.  Jose Guillen has eight home runs, but he also has 40 strikeouts.

Of the list, I would say Pedroia has been the best after falling behind.  That he has struck out only 13 times in 106 at bats is a testament to his ability (David Eckstein has the fewest strikeouts with nine in 122 at bats).  If I had to choose someone not on this list it would be Vladimir Guerrero, who has a 0.300 batting average, five home runs, 24 RBIs, and 19 strikeouts in 110 at bats.

BEST HITTER WITH TWO OUTS (minimum 50 at bats)

  1. Joey Votto (0.484 OBP; 11 HRs; 26 RBIs; 0.396 batting average; 17 BBs; 24 Ks)
  2. Albert Pujols (0.481 OBP; 11 HRs; 29 RBIs; 36 BBs; 14 Ks)
  3. Geovany Soto (0.479 OBP; 2 HRs; 9 RBIs; 20 BBs; 6 Ks)
  4. Ian Kinsler (0.471 OBP; 3 HRs; 14 RBIs; 17 BBs; 12 Ks)
  5. Billy Butler (0.462 OBP; 4 HRs; 17 RBIs; 21 BBs; 20 Ks)

Another interesting collection of players.  Soto makes it in just barely above the at-bat minimum and he is rebounding from an abysmal 2009 season.  But overall he is still behind his 2008 numbers.  Most of the country has probably not heard much about Billy Butler, but he is quietly putting together a nice season in Kansas City.  The other three are all-stars.

Albert Pujols joins the list again, but it is Joey Votto that takes this category.  Votto leads not only the OBP category, but he leads all hitters in average and is tied with Pujols in HRs.  Pujols does have Votto beat in walks drawn and he has fewer strikeouts, but it is clutch hitting like this that make MVP calls for Votto not that far fetched, especially given how well the Reds have played this year.

And now, just to round things out, here are the most clutch starting pitchers from the first half of the 2010 season.

BEST PITCHERS AFTER FALLING BEHIND IN THE COUNT (minimum 15 innings pitched)

  1. Vincente Padilla (1.3 K/BB Ratio; 13 Ks; 14 hits; 5.06 ERA; 16 IPs)
  2. Zack Greinke (0.955 K/BB Ratio; 21 Ks; 37 hits; 2.65 ERA; 37.1 IP)
  3. Roy Halladay (0.947 K/BB Ratio; 18 Ks; 46 hits; 4.03 ERA; 29 IP)
  4. Jered Weaver (0.857 K/BB Ratio; 24 Ks; 32 hits; 5.10 ERA; 30 IP)
  5. Cliff Lee (0.833 K/BB Ratio; 5 Ks; 21 hits; 3.48 ERA; 20.2 IP)

If you look at the ERA of pitchers in this situation, you would get pitchers like Mike Pelfrey (not bad), Josh Johnson (he’s good) and Tom Gorzelanny (who?).  So, I looked at strikeout-to-walk ratio because it shows if the pitcher lost the hitter to a walk or was able to bounce back.

Four of the top five are not surprising — Greinke, Halladay, Weaver, and Lee are all elite pitchers.  But, Padilla?  The Dodgers’ opening day starter knows how to get out of a jam.  But of these five, I will give the nod to the Royals ace.  While Greinke does seem to get himself behind in the count often (37.1 IP means it happens too often), he sports a very solid ERA in those situations and limits the damage.

So, to conclude, overall Albert Pujols is the most clutch hitter of the first half, with a honorable mention to Zack Greinke as the most clutch pitcher of the first half (cannot give it to him outright because I only examine one stat).   By the way, when the two met Pujols was 0-3 with a walk.  I’d give the edge to Greinke.