Leafing Through Pages: Analysis of Sports and Other Topics


A Brief Sabermetric Introduction

Filed under: Baseball — David Hunter @ 12:01 PM
Tags: , , , , , , , , , , , ,

Here’s a brief overview of the modern sabremetric statistics being used. First we’ll start with the more well known and transition on to the more complex.

On Base Percentage: Basically is how often the player gets on base, a goal in order to score runs. The more times one gets on base (and doesn’t create an out), the higher the chance of scoring a run. This is often calculated as (H + BB + HBP)/(AB + BB + HBP + SF). That’s the more basic and commonly used version. Other formulas have tried to incorporate Intentional Walks and GIDP (Ground Into Double Plays) for a more complete formula. An example of the latter would be (H + BB + HBP + IBB)/(AB + BB + HBP + IBB + GIDP + SF).

According to the first formula, Derek Jeter put up a .373 OBP in 2002. If you calculate the latter formula, his OBP becomes .367 but is still represented as a solid number.

Slugging Percentage: Basically the number of bases a player gains with their hits. A single counts as 1 base, a double counts as 2, a triple counts as 3, and a home run counts as 4. To calculate this, you divide total bases (e.g. 1B + 2B*2 + 3B*3 + HR*4) by at bats. So using Derek Jeter’s 2002 season as an example, he had 147 Singles, 26 Doubles, 0 Triples, and 18 Home Runs. Therefore the formula would be (147 + 52 + 0 + 72)/(644) for a SLG of .421.

On Base + Slugging: This is adding the OBP to SLG to get what is commonly referred to as OPS. Recent studies of the importance of OBP have confirmed that OBP is roughly 1.5 to 2 times more important than SLG. As a result, some sabermetricians like Tango Tiger have calculated OPS as OBP*1.7 + SLG to help include the importance.

Secondary Average: An attempt to try and calculate how many bases a player gains in a game overall, as opposed to just hits (e.g. Batting Average) or walks (e.g. OBP). This is calculated as (TB – H + BB + SB – CS)/AB. The resulting number is often similar to the range of BA, where any number over .400 is great and anything below .230 is awful.

Derek Jeter in 2002 put up a .297 BA and .373 OBP. His Secondary Average comes out to .283, a solid season but not outstanding. His 2002 teammate, Jason Giambi put up a Secondary Average of .479 in part because he walked a lot (109 BB) and hit for more TB (335 to Jeter’s 271).

Total Average: An attempt to weigh the extent of a player’s ability to contribute offensively while limiting the outs they make. It was created by Thomas Boswell in the 1970’s and is similar to other offensive sabermetric tools. It is calculated as [(TB + HBP + BB + SB) – CS/[(AB-H) + CS + GIDP].

Once again we’ll compare Derek Jeter and Jason Giambi. Derek Jeter’s Total Average in 2002 comes out to .809 and Jason Giambi’s Total Average is 1.136. Again, it’s another offensive statistic that gives additional weight to a player who walks and can hit for power.

Gross Production Average: A statistic created by Aaron Gleeman in order to refine OPS and achieve a more accurate representation of the importance of OBP in comparison to SLG. This statistic is calculated as (OBP*1.8+SLG)/4 and is very comparable to a BA. Anything over .325 is great, anything below .250 is bad.

In 2002 Derek Jeter’s GPA was .273, solid but not that great. His teammate Alfonso Soriano put up a GPA of .286 despite a lower OBP because he slugged .547 to Jeter’s .421. Finally Jason Giambi put up a GPA of .345 because he had a .435 OBP and .598 SLG.

Equivalent Average: A complex offensive statistic that measures offensive value per out and is adjusted for league offense, home park, and team pitching. It includes batting value and baserunning value. The link explains it in more detail but the statistic was created by Baseball Prospectus.

Runs Created: Basically tries to calculate how good a player is at creating runs for his team. The simplest equation is (H + BB) * TB/(AB + BB). The link also offers more advanced RC formulas that try to paint a more complete picture by including weighted aspects and HBP, stealing bases, GIDP, and SF.

Using the very basic RC, in 2002 Derek Jeter created 100 runs. So a lineup roughly of 9 Derek Jeters would score 900 runs in a 162 game season. Jason Giambi created 143 runs. So a 9 man lineup of Giambis would score roughly 1287 runs in 162 game season.

On the pitching side of things, sabermetrics has also been advancing statistics to better show a player’s ability.

WHIP: Basically divides the BB and H by IP. The lower the WHIP, the better as a pitcher doesn’t allow a hitter to get on base. Think of it as a reverse OBP.

2002 Mike Mussina put up a 1.19 WHIP whereas David Wells put up a 1.24 WHIP. Wells allowed 255 H+BB in 206.1 IP while Mussina allowed 256 in 215.2 IP.

HR/9, BB/9, and K/9: Basically averages the HR allowed, Walks allowed, and Strikeouts and divides them by the IP. Then it’s multiplied by 9 to simulate what that pitcher would put up in a complete game 9 inning performance. It’s a good tool to use if a player is unlucky and struggled in his record or ERA to see if his “rate” statistics remained the same or if he suddenly struck out fewer batters or started allowed more home runs than usual. Traditionally, you want a player below 1.0 in HR/9, around 2-2.5 in BB/9, and over 8.5 in K/9. A player under 5.5 K/9 will usually struggle long term career wise unless they have an amazing ability to not allow home runs or walks.

In 2002, Mike Mussina had 215.2 IP with 27 HRA, 48 BB, and 182 K. Thus his HR/9 would be (27/215.2)*9 for 1.13, his BB/9 would be (48/215.2)*9 for 2.01, and his K/9 would be (182/215.2)*9 for 7.61. He would finish the season at 18-10 with a 4.05 ERA.

Compare that to David Wells who went 19-7 with a 3.75 ERA in 206.1 IP. He had a HR/9 of 0.92, a BB/9 of 1.97, and a K/9 of 5.98. As you can see here, Wells allowed fewer home runs and walked fewer batters which probably helped lower his ERA in comparison to Mussina. Mussina’s higher strikeout rate would make him a better bet to produce similar statistics the following season though in comparison to Wells.

Defense Independent Pitching Statistics: More commonly referred to as DIPS, this was created by Voros McCracken to better evaluate pitchers on their own merits (e.g. independent of what their defense behind them contributed). As a result, this was transitioned into Defense Independent ERA or dERA.

Due to the complexity of calculating the above, Clay Dreslough created a formula called DICE or Defense Independent Component ERA. It is calculated as 3.00 + (13*HR + 3*(BB+HBP) – 2*K)/IP.

Tom Tango, also known as Tango Tiger, further refined the above into a statistic called Fielding Indepenent Pitching or FIP. It is simpler and calculated as 3.20 + (13*HR + 3*BB – 2*K)/IP.

The Hardball Times essentially uses DICE but makes it 3.20 at the beginning, rather than 3.00.

In general, the lower the ERA, the better the pitcher may be in general thus if he has a higher ERA than his DICE/FIP, odds are that his defense may have hurt him. The inverse is when his ERA is much lower than his DICE/FIP where his defense may have helped him a lot.

Let’s compare Mike Mussina and David Wells again from their 2002 seasons. Mussina put up a 4.05 ERA and Wells a 3.75 ERA.

Mussina DICE = 3.68 and his FIP (Hardball Times version) = 3.88. David Well’s DICE = 3.72 and his FIP (Hardball Times version) = 3.92. Here we see that Mussina was arguably was a better pitcher than Wells but struggled more due to the Yankees’ defense behind him.



The Role of the Lead Off Hitter and Batting Orders

Everybody knows that the role of a great leadoff hitter is to not only get on base, but score runs whether it be through advancing to third on a line drive single or stealing second and getting himself into scoring position. Even today, the role has changed as Larry Bowa points out in the lack of an ability to draw a walk.

Here’s an interesting blog post analyzing the 2009 season and how the leadoff hitters fared from every single major league team.

Sabermetricians often like to point out players such as Juan Pierre and his lack of success and general disappointment as a leadoff hitter. In that article there’s a mention of Jerry Owens who hit leadoff for the White Sox in 384 PA in 2007. He hit .268 with a .325 OBP and stole 32 bases in 40 opportunities.

Juan Pierre in 2008 hit lead off in 283 PA. He hit .261 with a .293 OBP and stole 28 bases in 34 opportunities. Last year he was markedly improved in 254 PA where he hit .314 with a .372 OBP. He also drew 16 BB and 2 IBB compared to 11 BB and 0 IBB in 2008.

There’s also been examples of players who don’t steal bases who have made very effective lead off hitters. Kevin Youkilis in 2006 hit lead off in 467 PA. He would hit .286 with a .385 OBP and walked 63 times with 0 IBB.

He actually hit worse batting 4th and 5th with a BA of .241 in 112 AB and 131 PA. He also drew 18 BB at those spots.

One interesting facet is the role of a team’s batting order. The 2000 Toronto Blue Jays lineup studied in the link showed that the optimal lineup in comparison to the worst was a difference of as many as 4 wins. Thus it could be the difference between an 82 win season and a wild card berth at 86 wins.

Also pointed out was the best overall hitter, Carlos Delgado, was most effective in the leadoff position. This is likely in part due to his ability to not only get on base but also his power. A player able to get on base and hit for power, given an additional number of AB (roughly at least 81 if he gets an additional AB in half of his games played), would also help the team score more runs in that instance.

Another article also points out the case of batting order and makes some interesting observations initially. The best hitters are often the 3rd, 4th, and 5th as they usually hit for high power and drive in higher RBI as a result in the eyes of most managers. The next two best hitters are generally hitting leadoff and 2nd, because they can get on base and score runs for the 3/4/5 hitters.

All of that is fairly conventional and expected. It’s shown that that range expected over the course of a season was roughly a difference between 643 runs to 671 runs, 28 runs and roughly 2-3 additional wins or additiona losses in the case of the former.

A big deal this offseason has been about pushing Jose Reyes down in the order. Traditionally he’s been a lead off hitter but talk has been about moving him down to the 3rd spot due to his increased power.

In 2005 he hit .273 with a .299 OBP and drew 25 walks. In 2006 he hit .301 with a .354 OBP and drew 52 walks. He also had 19 home runs. 2007 he hit .280 with a .354 OBP and drew 77 walks. He also hit 12 home runs. In 2008 he hit .297 with a .358 OBP and drew 66 walks. He also hit 16 home runs.

He’s been a very solid lead off hitter, even with his mid teen HR power, in part because he gets on base almost 36% of the time and draws a good amount of walks. Rumored replacements include Carlos Beltran who offers 25+ HR power but also has put up OBPs of .376 and .415 the last 2 seasons, and Luis Castillo who’s in the mold of the “traditional” lead off hitter. He rarely hits for more than 3 HR a season but can walk 65-70 times and has got on base at a .355 and .387 clip the last 2 seasons.

It remains to be seen how lineups are created by Opening Day of 2010 and it’s worth keeping an eye out on who gets the coveted lead off roles for your favorite teams. Does the manager go with a guy who can on base at a high percentage or a guy who can steal 70 or 80 bases? As a final reference, the 2001 Seattle Mariners who won 116 games had the player batting lead off put up the third best OBP on the team despite drawing just 33 walks.


Examination of the Steroid Era and Finding Other Causes

Many knowledgeable baseball fans know that the early 1990’s but especially the mid to late 1990’s are shrouded in allegations that vary from 30% of players to “hundreds” to everybody were on steroids during those eras. Let’s take a look at the factors that helped usher in the “steroid era” and see if anything can be partly explained.

The First Examination: The Ballpark Effect

The massive influx of ballparks mostly occurred starting in 1994. The Florida Marlins came into existence in 1993 with park factors of 101, 108, and 98 from 1993 to 1995. Joe Robbie Stadium also saw some key fence changes occur.

In 1993 the fences measured at 335 in LF, 380 in the power alleys, 410 in CF, and 345 in RF. A big change was made for the 1994 baseball season. LF saw the fence moved closer by 5 feet, the power alleys pushed back 5 feet, and CF moved in 6 feet. The net result was a closer fence in LF and CF while the power alleys remained reachable.

1993 saw a percentage of 1.6% HRs at home by the Florida Marlins. In 1994, with the fence changes, that number jumped to 2.3% and 1995 was 2.8%.

The Colorado Rockies also arrived in 1993. Mile High Stadium’s park factors were 120 and 113 in 1993 and 1994. The move to Coors Field saw that park factor hit 128 and 129 in 1995 and 1996.

Ballpark LF Fence CF Fence RF Fence
Mile High Stadium 335 423 375
Coors Field 347 415 350

Once again, the new ballpark also featured new dimensional changes. The net result was a +21 improvement in dimension. Another big change were the fence heights which went from 12 feet in LF and 30 feet in CF down to 8 feet in LF and CF at Coors Field.

The 1994 season saw a HR % of 3.0 at home by the Rockies. In 1995 that jumped up to 5.3% and 1996 saw 5.1% from the Rockies alone.

New ballparks also came from other franchises. The Baltimore Orioles moved from Memorial Stadium with dimensions of 309 down the foul lines, 378 in the power alleys, 405 in CF to Oriole Park at Camden Yards in 1992. In 1992 the dimensions were 333 in LF, 364 and 373 in the power alleys, 400 in CF, and 318 in RF. The dimensions down the line were slightly pushed back but the power alleys and CF were moved in. Another change was making the fence 7 feet everywhere but RF.

In 1991 the Orioles had a 3.0 HR% at Memorial Stadium. That number shifted to 2.8% in 1992 and up to 3.2% in 1993.

Here’s a quick table showing the dimensions and key fence heights during the early and mid 1990’s. You’ll see that many also had shorter fences. Note that LF and RF means to the foul pole.

Ballpark Year LF CF RF Fence Height Notes
Atlanta-Fulton County 1995-1996 330 400 330 10 Feet
Turner Field 1997 335 401 330 8 Feet
Tiger Stadium 1999 340 440 325 9 Feet
Comerica Park 2000 345 420 330 8 Feet except Right Center at 11
3 Rivers Stadium 2000 335 400 335 10 Feet
PNC Park 2001 325 399 320 LF is 6 Feet. Left Center and CF is 10 Feet. RF is 21 Feet.
Kingdome 1998 331 405 312 LF and CF 11.5 Feet. RF 23.25 Feet.
Safeco Field 1999 331 405 326 8 Feet
Arlington Stadium 1994 330 400 330 11 Feet
Rangers Ballpark 1995 334 400 325 LF 14 Feet. CF and RF 8 Feet.
Old Comiskey Park 1990 347 409 347 LF and RF 9.8 Feet. CF 18 Feet.
New Comiskey Park 1991 347 400 347 8 Feet

That was just a quick table but you can see the dramatic shift in fence heights and field dimensions for a handful of ballparks. Even tiny changes were occurring like RF being shifted in Candlestick Park from 335 Feet down to 330 in 1991 down to 328 in 1993. The fences there were also altered from 9 in 1984 down to 8 in 1993. Busch Stadium wasn’t immune either. CF was shifted from 414 Feet to 402 Feet in 1992. The fences were all shortened from 10.5 to 8 feet in 1992 as well.

The Second Examination: The Expansion Teams

In 1993 the Colorado Rockies allowed 181 total home runs along with a .294 Batting Average. The 1993 Florida Marlins allowed 135 total home runs along with a .261 Batting Average. In 1993 the National League averaged 1 HR every 40 At Bats. The year before, the National League averaged 1 HR every 52 At Bats. That’s an increase of 12 At Bats for every HR hit.

The Rockies allowed 1 HR every 31 AB and the Marlins allowed 1 HR every 41 AB. While the Marlins were helpful to opponents, the Colorado Rockies were a big benefactor in pushing the HR totals up. Also largely in part due to the additions of Mile High Stadium and Coors Field.

In 1998 came two more franchises in the Arizona Diamondbacks and Tampa Bay Devil Rays. Arizona allowed 1 HR every 29 At Bats and Tampa Bay allowed 1 HR every 32 At Bats.

Major League wide, the 1997 season saw 1 HR every 33 At Bats and the 1998 season saw 1 HR every 33 At Bats as well. Once again, one team was “league average” whereas the other team was quite a bit higher and thus helped push the Home Run totals up.

The Third Examination: Players Are Trying to Hit for Power

The fact is that players in the mid 1990’s and even to the present are trying to hit more home runs and are less willing to just hit for contact. As a result, this drives up the batting average due to more extra base hits and also drives up the slugging percentage.

1991: 1 HR per 42 AB and a .256 BA vs. .385 SLG
1992: 1 HR per 47 AB and a .256 BA vs. .377 SLG
1993: 1 HR per 38 AB and a .265 BA vs. .403 SLG
1994: 1 HR per 33 AB and a .270 BA vs. .424 SLG
1995: 1 HR per 34 AB and a .267 BA vs. .417 SLG
1996: 1 HR per 32 AB and a .270 BA vs. .427 SLG
1997: 1 HR per 33 AB and a .267 BA vs. .419 SLG
1998: 1 HR per 37 AB and a .266 BA vs. .420 SLG
1999: 1 HR per 30 AB and a .271 BA vs. .434 SLG

Even in the 2009 Season MLB saw a .262 BA and .418 SLG. Teams also hit 1 HR every 33 AB. Players at positions that used to be considered strong glove and weak bat such as Second Base or Short Stop saw a revolution in the mid to late 1990’s. A large part of this was the talent emerging…

In 1993 you had Mike Piazza emerge at Catcher with 35 Home Runs. Todd Hundley would suddenly come out of nowhere with 41 and 30 home runs in 1996 and 1997, seasons that saw Piazza also hit 36 and 40 home runs himself. Even less talented catchers like Charles Johnson were hitting 19 in 1997 and 1998. In 2000, Johnson hit 31 and that has carried over to modern day catchers such as Brian McCann.

At 2B you also saw the emergence of Jeff Kent who went from 21 in 1993 to 29 and 31 in 1997 and 1998. Craig Biggio started hitting 20-22 home runs from 1993 to 1998. Once again, this has also carried over to current players such as Chase Utley and Dustin Pedroia who hit 17 in 2008.

The biggest change, however, was at the Short Stop position. While Cal Ripken Jr was a revolution, the generation in the mid 1990’s took power to another level. Derek Jeter hit 19 and 24 home runs in 1998 and 1999. Nomar Garciaparra was hitting 30 and 35 in 1997 and 1998. Alex Rodriguez was hitting 36 in 1996 and following that up with 42, 42, and 41 from 1998 through 2000. Even guys like Barry Larkin were getting into the act as he hit 33 in 1996 and 17 in 1998. A career high and third best total.

Combined with the closer park dimensions and shorter fences, players were going for the power numbers that not only helped them win games but also got more money in free agency. Barry Larkin was making almost $6 Million in the mid 1990’s. Alex Rodriguez parlayed his power into a career where he has made at least $22 Million a year since 2001.

Power was no longer just for guys who were playing at first base or playing in the outfield. Now power was coming from every single position on the baseball diamond and from every single player in the lineup. Tony Gwynn, largely known as a singles hitter, suddenly was hitting 17 and 16 home runs in 1997 and 1998.

The Conclusion

Yeah, steroids and human growth hormone were fairly prevalent during the 1990’s and that fact can’t be denied. But to place all of the blame on steroids or other performance enhancing drugs for the explosion of home runs is narrowing the scope too much. The increase in ballparks favorable to hitters, coupled with expansion teams that spread out the talent, and the general belief that shifting to hitting for power would earn more money all helped result in the modern day “era” where power largely reigns supreme.


Fantasy Baseball and the Role of Park Factors

Many baseball fantasy leagues rely on daily roster changes (e.g. you set your roster up Monday, then Tuesday) as opposed to a weekly format (e.g. you set your roster for the week on Monday). As a result of this facet, a smart fantasy owner can easily utilize both platoons at a scarce position such as second base, or depend on a player who plays extremely well at home compared to the road or vice versa.

One great thing is that such savvy owners can also get these average players otherwise late in their drafts or off the free agent heap and ride their splits to solid totals.

Paul Konerko will be 34 years old but rides a fairly good split and could be a useful bench role player. Here are his splits over the past 3 seasons, two of which he hit for less than a .260 BA.

2007: .258/.359/.504 with 38 R, 17 HR, 43 RBI, 0 SB at home vs. .260/.343/.477 with 33 R, 14 HR, 47 RBI, 0 SB on the road
2008: .285/.403/.575 with 36 R, 15 HR, 38 RBI, 1 SB at home vs. .204/.295/.331 with 23 R, 7 HR, 24 RBI, 1 SB on the road
2009: .271/.354/.524 with 42 R, 18 HR, 50 RBI, 0 SB at home vs. .282/.352/.454 with 33 R, 10 HR, 38 RBI, 1 SB on the road

You can clearly see that he’s hit more consistently at home and has been incredibly more productive in terms of home runs and runs batted in. Combine that with a similar splits heavy hitter and you could get a 1B worth 30-40 home runs and 70-85 RBI when combined.

Anybody who plays fantasy baseball, however, has heard of the Lima Plan created by Ron Shandler primarily because of Jose Lima’s 1998 season in which he went 16-8 with a 3.70 ERA.

A smart fan will note he went 9-3 with a 3.16 ERA at home but just 7-5 with a 4.33 ERA on the road. That’s where the following players, who can all be had for very cheap, come into proverbial play.

Kevin Slowey is well known for his incredibly low walk rate and solid strikeout rate. He also has gone 22-14 the past 2 seasons, so where’s the rub? It’s all about the splits.

2008: He went 7-4 with a 3.38 ERA and 6.91 K/9 at home vs. a 5-7 record with a 4.52 ERA and 6.97 K/9 on the road.
2009: He went 8-0 with a 4.78 ERA and 6.51 K/9 at home vs. a 2-3 record with a 5.01 ERA and 9.25 K/9 on the road.

He offers a better strikeout rate in road games but at the expense of key stats for pitchers, wins and ERA. He’s 15-4 at home vs. 7-10 on the road and a smart fantasy owner will take the wins and lower ERA everytime.

The key here is to build your squad with capable hitters and then zero in on “average” or even below average starting pitchers where you can rotate based on matchup. As long as your league has no restrictions on moves, you can play mix and match to an easy run.

Other similar pitchers include guys like Paul Maholm. Sure he’s only gone 17-18 the past 2 years but let’s dig much deeper.

2008: 7-2 with a 3.36 ERA and 5.13 K/9 at home vs. 2-7 with a 4.13 ERA and 7.24 K/9 on the road
2009: 4-3 with a 3.50 ERA and 5.39 K/9 at home vs. 4-6 with a 5.54 ERA and 5.66 K/9 on the road

Now if you only started him at home the past 2 years; you’d have gotten a pitcher with 11 W, a very solid ERA, and slightly below average K rate.

Here’s a list of similar pitchers to key in on later in the draft as possible sleepers. Some of these guys struggled total wise last year but still had good splits.
Brian Bannister
2008: 5-8 with a 3.96 ERA and 5.12 K/9 at home
2009: 4-5 with a 4.22 ERA and 5.34 K/9 at home
Chad Billingsley
2008: 10-4 with a 2.95 ERA and 10.06 K/9 at home
2009: 8-6 with a 4.01 ERA and 8.71 K/9 at home
Gavin Floyd
2008: 10-3 with a 3.55 ERA and 7.37 K/9 at home
2009: 6-3 with a 2.47 ERA and 8.80 K/9 at home
Chris Young
2008: 3-3 with a 2.35 ERA and 10.17 K/9 at home
2009: 4-1 with a 2.61 ERA and 7.34 K/9 at home

While I’ve mainly focused on starting pitchers as you can win with quantity over quality, you can also do the same for relief pitchers and closers too.

Leo Nunez was much better on the road in 2009 going 2-1 with a 3.06 ERA and a K/9 of 8.41. Even top tier closers such as Jonathan Papelbon (1.38 ERA in 32.2 IP on the road in 2009) and Ryan Franklin (1.85 ERA in 34 IP at home in 2009) showed splits where they were better at home or on the road.

If you play in a league that supports holds, you can key in on guys like Joe Thatcher who put up a 2.45 ERA and a K/9 of 12.50 at home in 2009 or Tony Sipp who put up a 1.23 ERA and 12.27 K/9 at home in 2009 for Cleveland.

Whether it be a hitter like Paul Konerko, a starting pitcher like Kevin Slowey, or a reliever like Joe Thatcher, a smart fantasy owner will know the park factors that effect his players and his team. He’ll utilize those park factors to his advantage and such rotations can make winning championships much easier than trying to rely on the whole season of an average or below average player.


The Dominican Republic and Baseball Plate Discipline

The phrase used to be, and arguably still remains, that if you were a baseball player in the Dominican Republic, you had to hack (or swing and hit) your way off the island in order to make it with a Major League team. Such a phrase seemingly came from scouts, coaches, and general managers but was also manifest through the free swinging ways of many of the players who arrived from the Dominican Republic.

Vladimir Guerrero is often pointed as a reference point when such a phrase comes up, whether it be in an interview or a general comment. What’s interesting is that Guerrero has shown an ability to work on his plate discipline. Here are his plate appearances per walk from 1998 when he had 677 PA with Montreal through 2003 before he went to Anaheim.

1998: 16 PA per BB (677 to 42)
1999: 12 PA per BB (674 to 55)
2000: 11 PA per BB (641 to 58)
2001: 11 PA per BB (671 to 60)
2002: 8 PA per BB (709 to 84)
2003: 7 PA per BB (467 to 63)

In a period of 6 seasons Guerrero managed to improve his patience at the plate from 1 walk every 16 plate appearances to peaking in 2003 at 7 plate appearances for every walk. Fangraphs shows that in 2002 Guerrero swung at 32.6% of pitches outside the strike zone and swung at 34.8% of pitches outside in 2003. That number dropped further in 2004 to 29.9% and 2005 to 32.2% before suddenly jumping up to 40% and 45% where he’s often been through the present.

Miguel Tejada is a more prominent example of the term. He entered the majors for good in 1998 and proceeded to walk 28 times in 407 PA (6.9%). He then improved his ability to draw a walk in 1999 (8.5%) and peaked in 2000 at 9.7% with 66 walks. Rather than continuing his climb, which saw his On Base Percentage rise from .298 to .349 in just 2 seasons, he reverted and seemingly has regressed in his career. Now he relies on a high batting average to maintain a solid On Base number while drawing the occasional walk.

Even in recent years that trend has seemingly continued with players such as Fernando Martinez who has walked 80 times in 1204 plate appearances in the minor leagues (6.6%) and Wilkin Ramirez who has 171 walks in 2497 plate appearances (6.8%) in minor league play.

Other pro players such as Adrian Beltre with 478 walks in 6877 career plate appearances (7.0%) and Angel Berroa with 117 walks in 2807 career plate appearances (4.2%) have seriously struggled with maintaining enough plate discipline to be selective pitch wise and draw walks when the opportunity arises.

This is not to say that players coming over from the Dominican Republic can’t develop the plate discipline and eye to succeed. Players such as Luis Castillo with 761 walks in 7172 plate appearances (10.6%) with a career .369 OBP have shown that Dominicans can carve out very successful careers. He’s had 7 seasons drawing over 60 walks and has parlayed that into what will be his 15th season in the Majors. David Ortiz showed promise from 2000 through 2002 with a walk rate of 10.8% before blossoming as a full time player with the Boston Red Sox and going 3 straight years with over 100 walks.

Given the rise of importance related to the On Base Percentage statistics within Major League Baseball front offices, it remains to be seen whether that will eventually carry over to Dominican Republicans and their ability to transition from free swinging to patience to get off the island. Jerry Crasnick of ESPN offers up an interesting tidbit at the end of an article related to plate discipline focusing on the Oakland Athletics Latin-American academy.

Next Page »

Create a free website or blog at WordPress.com.