Maybe Red Sox hitters are better able to take advantage of the Green Monster, thereby giving Fenway a larger home field advantage because of its quirky dimensions. Like much of my work this is heavily indebted to earlier work on the subject by John Walsh. In early 2007 he looked at which Red Sox hitters take the most advantage of the Green Monster and also which non-Red Sox would benefit most by hitting at Fenway.

Percent of balls in air towards Green Monster

If Red Sox do take advantage of the green monster than you would expect them to hit more balls in its direction, with RHBs trying to pull more balls and LHBs trying to go the other away with more when they are home than when they are away.

Here is the frequency distribution of the angle of fly balls and line dives to the outfield by RHBs . The plot on the left is for Red Sox hitters when home and away, and on the right for all visitors at Fenway and all non-Red Sox teams when they are at home. I use the same -45 (3B line), 0 (2nd base), 45 (1st base) orientation for my last post. The Green Monster is indicated in green.

It looks like visitors at Fenway change their approach much more than Red Sox hitters. Red Sox hitters' home and away spray patterns are virtually indistinguishable, but for visitors the spray pattern is shifted a degree or two toward third base when batting at Fenway. I assume this is caused by these hitters trying to pull the ball more, but it could also be a result of Red Sox pitching (maybe they pound the inside of the zone more than the average pitcher).

Here is the same figure for left handed batters.

Both Red Sox and visiting lefties hit slightly more balls in play down the left field line at Fenway than elsewhere. Going along with that is a slight drop in the number of pulled balls in play at Fenway for both groups. The effect is subtle, but it looks like lefties might make some effort to go the other way more often at Fenway.

Here is an overview.

Proportion of outfield fly balls and line drives in direction of the Monster
+---------------------+--------+--------+
|                     |    RHB |    LHB |
+---------------------+--------+--------+
| Red Sox at Fenway   |  0.503 |  0.266 |
| Red Sox Away        |  0.511 |  0.257 |
| Visitors at Fenway  |  0.507 |  0.287 |
| Non-Red Sox at Home |  0.477 |  0.278 |
+---------------------+--------+--------+

Here you can see that Red Sox righties actually hit fewer balls in play in the direction of the Monster at Fenway than on the road. That is very surprising. Visiting RHBs see a big jump in their balls in play to that direction. For both Red Sox and visiting lefties there is a small increase in balls in play to that direction at Fenway compared to elsewhere.

Percent that actually hit monster

Ok so visiting hitters are hitting more balls in play towards the Green Monster, but are they actually getting more hits off it? I used the same technique as John Walsh and classified a ball in play as one off the monster if it was a fly ball or line drive that was a hit and fielded within 25 feet of the Monster (I am using the gameday batted ball locations with Peter Jensen's translation factors). When John went back and checked this he found that about 60% of the 'hits off the monster' were really that, so these numbers will be over estimates. But I don't think they will systematically over or under estimate Red Sox compared visitors.

Using such a definition here are the percentage of batted balls that I classified as 'hits off the monster.'

Proportion of balls in play that are hits fielded within 25 ft of the Monster
+---------------------+--------+--------+--------+
|                     |    RHB |    LHB |    All |
+---------------------+--------+--------+--------+
| Red Sox at Fenway   |  0.054 |  0.037 |  0.046 |  
| Visitors at Fenway  |  0.060 |  0.041 |  0.052 |
+---------------------+--------+--------+--------+

These numbers seem very high, so I am sure that I am overestimating the number of Monster hits by quite a bit. Still it seems that visitors, both lefties and righties, get more hits off the Green Monster than Red Sox hitters. This seems very counter intuitive. If these hits would have been outs elsewhere the Green Monster is giving visitors an advantage. On the other hand if visitors are changing their approach at the plate to get more hits off the Monster maybe their contact to other areas is weaker.

Home Runs Over the Monster

The other thing the Green Monster offers is a short, but high, porch to hit HRs over. If Red Sox hitters can adapt their swings to hit more HRs over it, that could be where the advantage shows up. Here is the HR rate per ball in the air by angle, just in Fenway.

Now here is a big advantage to Red Sox hitters. Over the length of the Green Monster Red Sox righties have a big HR/BIA advantage over visitors. In the rest of the field, expect for just along the right-foul line, there is little difference in HR-rate. Does it look to you like Red Sox righties tailor their swings to getting HRs over the Green Monster?

The next step would be to put it all together. How much do the additional HRs by Red Sox hitters weigh against the additional hits off the Monster by visitors? Could we calculate the value of the Monster to the Red Sox in such a calculation? Maybe another day.

That led to a four-inning shutout stint against the White Sox during last game of the season (September 28). The starting appearance may have earned the 37-year old Dean a nice chunk of cash, as he supposedly received a percentage of the gate. Nearly 16,000 paid to see Dean's junkball display in a game that would have normally drawn 3000 to 4000 diehards. Despite Dean's gutsy effort, the Browns lost 5-2 to finish the season in last place with a 55-99 (.357) record.

With injury-shortened seasons of 10, 13 and 19 games, Dean's career boils down to six full seasons and 45 additional appearances. A 30-7 effort with the Cardinals in 1934 plus a 1952 movie (The Dizzy Dean Story) on his life combined with Dizzy's colorful personality and popularity as a pioneering TV baseball announcer provided enough momentum for Cooperstown enshrinement in 1953.

Raul Chavez will never be ranked among baseball's greats, but he has qualified for an appearance on a future Hall of Fame ballot. The Blue Jays backup catcher is now in his 11th big league campaign. As of June 22, Chavez has played just 230 games in the Show.

The Venezuelan-born Chavez is now in his 19th professional season, and the bulk of his 1383 minor league games have come at the AAA level. Chavez saw his first AAA action with Tuscon of the Pacific Coast League in 1995, and he has played 915 games with eight AAA teams in 12 seasons (1995-2003, 2005, 2007-09).

It was on to Ottawa of the International League in 1996, and Chavez made his major league debut with the Expos as a late season call-up. That four-game cup of coffee led to 13 more appearances with Montreal in 1997.

1998 was split between Ottawa, Tacoma and a single game with the Mariners. Chavez spent all of 1999 in Tacoma before signing with the Astros in the offseason. 2000 was the first of Chavez's four consecutive seasons with the New Orleans Zephyrs. He played 14 games with Houston in 2000 and made a pair of big league appearances in 2002 before playing 19 games with the Astros in 2003.

2004 was the high point of the stocky catcher's career. Chavez spent the entire year in Houston - his only full major league season. A .210 average (34 for 162) with 0 HR and 23 RBI in 64 games was the stat line.

It was back to AAA in 2005, as Chavez split the season between Round Rock and the Astros. A sub-Mendoza Line batting average (.172, 17 for 99) combined with Chavez's trademark lack of plate discipline (just four walks) led to a horrendous .210 OBP.

Even AAA would have looked good to Chavez in 2006, as he spent most of the year riding buses with Bowie of the Class AA Eastern League. That's quite a show of perseverance for a 33-year old player, and it was rewarded with a promotion to the Orioles.

The Yankees noticed Chavez and signed him to a AAA contract for 2007. A .221 average at Scranton was one reason why he never appeared in pinstripes that season. Chavez began 2008 at Indianapolis, but he spent most of the year with the Pirates. A .259 (30 for 116) performance in 42 games provided a nice boost to his career batting average.

A pair of games in Las Vegas kicked off 2009, and it marked Chavez's 18th year of minor league service. He was promoted to Toronto on April 18 and has done well in a reserve role. As of June 22, Chavez was hitting .263 (15 for 57) with 2 HR and 6 RBI. With just four strikeouts, making contact hasn't been a problem, but the free swinger is still looking for his first walk of the season.

A .227 lifetime average and .261 OBP means Chavez won't have to book a trip to Cooperstown in the future, but he will be able to ask his grandchildren "Do you know what Yogi Berra, Johnny Bench, Carlton Fisk, Mike Piazza, Pudge Rodriguez and I have in common? We were all on the Hall of Fame ballot!"

So what does Raul Chavez's many trips between AAA and the majors mean? It's a testimony to the determination of a 36-year old journeyman player.

Some readers suggested looking only at players' pre-free agency WAR to make the model more useful to major league teams. Others suggested that the advantage of college players over high schoolers has decreased over time. Still others wanted to see not only the expected value of a player's WAR, but the distribution of WAR's surrounding each pick. In this article, I intend to examine these suggestions and ideas to help provide a better understanding of the value of these draft picks.

Before I get started, I should say that I improved the quality of the data I was working with. I now have picks 1-50, every 5th pick until #100, every 10th pick until #500, and every 25th pick up to #1000 in my database. I also now have Sean Smith's full WAR database used to calculate WAR.

A Player's First 6 Years

First, as Tom Tango suggested, it's more useful to major league teams to have data on a player's first 6 years of WAR, rather than their career WAR, since the benefit of selecting a good player in the draft only lasts until they reach free-agency, after which a team must pay market value like everybody else. Here I fit the model using only the first 6 year WAR as the dependent variable (a year of service was defined as 130 AB, 20 games pitched, or 50 innings pitched in a season). As you might suspect, the data follows the same form and shape of the career WAR data. As it turns out, a player's pre-free agency WAR is almost exactly half of their career WAR. Both models are listed below:

Expected Career WAR = (21.67 + (-11.7 * pitcher) + (6.1 * college)) * selection ^ (-.54)

Expected First 6 Year WAR = (10.9 + (-5.1 * pitcher) + (3.1 * college)) * selection ^ (-.52)

where pitcher is equal to 1 if a player is a pitcher, college is equal to 1 if he is a college player, and selection is equal to the # overall selection in the draft.

As you can see, the shape, determined by the exponent, is nearly the same in both models. Additionally, the scale parameter is about half of what it was in the career model, as are the bonuses and penalties for college players and pitchers respectively. While a player earns only a small percentage of his total earnings in his first 6 years, he earns half of his career value. Because the shape of the models are the same, this seems to be true for players on all levels of the draft spectrum.

Changes Over Time

Over time, the draft has evolved, along with teams' scouting methods and drafting strategies. One interesting thing to examine is whether the parameters in the model would change over time. Have teams started drafting more efficiently as time goes on? Have pitchers been better draft selections over time? How about college players?

I adjusted my model to include a parameter for year. Since the overall WAR for a draft must necessarily stay relatively constant throughout time, I also needed to add a year parameter in the exponent. The new model was of the form:

Expected WAR = (a + (p * pitcher) + (c * college) + (y1*year)) * selection ^ (b + y2*year)

The result of the model was a significant positive parameter for the y1 variable, but a corresponding negative y2 value (y2 was not significant in a test, but as I mentioned, if we include y1, y2 must also be included to maintain the proper balance). This indicates that teams are now drafting more efficiently - high picks have a higher WAR than in years past, while low picks have a lower WAR than in years past.

According to the model, #1 selections in the year 2000 expect to have a career WAR of 26.1 , while #1 selections in the year 1970 were expected to have a career WAR of 19.4. However, as the rounds go on, this advantage decreases until after approximately pick #200, after which the old picks are expected to do better than recent picks. Overall, the result is approximately the same total WAR for both modern and old drafts, but the early picks are more valuable in recent drafts than in years past.

This makes sense because scouting methods and statistical analysis have given teams more accurate prognosticating abilities about a player's future major league potential. With this increase in information, the better players are drafted sooner, clustering the WAR distribution more heavily in the early part of the draft. Below, is a zoomed in graph of 1970 vs. 2000 WAR by draft picks where you can see the lines cross.

It's also been hypothesized that the value of pitchers and college players has changed over time. To test whether this is true, I added interaction terms to account for this possibility. The model now takes the following form:

Expected WAR = (a + (p * pitcher) + (c * college) + (y1*year) + (py*year*pitcher) + (cy*year*college)) * selection ^ (b + y2*year + p2*pitcher + c2*college)

A reader had suggested that college players were more valuable in the past, but that this advantage no longer existed. The model finds some evidence of this claim - the cy variable is negative, indicating a decrease in the relative value of college players over the years. Another way of looking at this is that highly drafted high school players have increased their value more rapidly than college players over the years. For #1 selections, high school hitters are expected to gain 20 more WAR now than in 1970, while this advantage decreases to only 10 WAR for college hitters. This result is not significant for the first-six-year WAR model, but it is significant for the career WAR model.

The value of pitchers over time however, has decreased strongly. Despite the fact that #1 picks as a whole have much a much higher expected WAR now than in prior years, the expected WAR of a pitcher drafted overall #1 is actually less than it was in the early years of the draft. The is in stark contrast to the strong increases over time for #1 hitters. Whether this is the result of teams trying extra hard to build "pitching organizations" or is due to other reasons, it appears highly drafting pitchers is an even riskier proposition today than when the draft began.

Below is a table of the two full models in determining the expected WAR by draft position.

Distribution of WAR by Pick

Also interesting is not only the expected WAR for each pick, but the probability of becoming a certain caliber player. Using a model of the logistic form, I estimated the probabilities of gaining a certain level of WAR. The model was of the form:

P(WAR) = exp((a+p1*pitcher+c1*college)*selection^(b)+int)/(exp((a+p1*pitcher+c1*college)*selection^(b) + int) + 1)

The models often had troubled converging, so the year terms and the exponential terms for pitchers and hitters were left out of the model. However, you can expect that they would have the same pattern as the models based on the expected value of WAR. Below you can see a graph of the probabilities of hitting various career WAR cutoff values, based on the above model. The graphs are for high school hitters.

As you can see the #1 overall selection has about a 2 in 3 chance of making a positive impact on a major league club. The probability for a decent impact of 10 WAR is 54%. The probability of a 30 WAR career, which is a career which probably includes a couple of All-Star appearances and several solid seasons is 29%. The probability of a 50 WAR career, which is close to that of a borderline Hall of Famer, is about 16%. Overall, there is a fair chance that a number one selection will never make an impact, but also a non-trivial probability that he will end up in the Hall of Fame. This indicates the obvious large variability in a player's potential career.

The chart above shows the model outcomes broken down by type of player and pick. One interesting finding is that pitchers are about as likely as hitters to make a positive impact on the major leagues with WAR>1. However, they start to slip when measuring the probability of having a great career. A college pitcher actually has a greater chance than a high school hitter of having a WAR>1 (71% vs. 68% for the #1 pick). However, the odds of having a WAR>30 are very much in the hitter's favor (9% vs. 29% for the #1 pick). While teams appear just as likely to get their pitching prospects to the majors, the probability of having a great career is quite small, even for top picks. This is the driving force behind the reasoning that teams should take hitters over pitchers in the draft.

For those more interested in players' pre-free agency WAR, below is a graph of this result, which largely follows the same shape as well as the same college/pitcher tendencies.

Conclusion

In conclusion, the following things can be said:

1) The first few draft picks are worth vastly more than later picks - a fact that is becoming more and more true as time goes by.
2) College players are a better bet than high school players, although this advantage has decreased through the years.
3) Pitchers, on the other hand, are less likely to bring value, a fact that is more true today than it was years ago.
4) Finally, highly drafted pitchers are about as likely as hitters to make a positive impact in the majors, but are much less likely to be truly great players.

I hope this study brings a greater understanding and insight into the value of draft picks and what type of player is likely to contribute at the major league level.

With the advent of Defense Independent Pitching Statistics (and DIPS 2.0) earlier this decade, analysts began to pay more attention to strikeouts, walks, and home runs allowed. As a result, Fielding Independent Pitching (FIP) is now recognized as a better measurement of value than wins, winning percentage, ERA, and ERA+. Some even prefer xFIP, Luck Independent Pitching Statistics (LIPS), or tRA, which, at a minimum, normalize HR/FB rates or break down the types of batted balls.

One can also value pitchers based on counting stats, such as Pitching Runs or Runs Saved Against Average. Runs can be converted into wins, giving us Win Shares, Win Shares Above Bench (WSAB), Wins Above Replacement (WAR), and Wins Above Replacement Player (WARP). The main differences generally involve the use of run estimators and definition of replacement levels.

I like looking at K, BB, and GB rates. Strikeouts exert a greater influence over pitching performance than walks and groundball rates, such that K > BB > GB. Within strikeouts, one can use K/9 (good), K/BF (better), or K/100 pitches (best). K/100P has a higher correlation to runs allowed than strikeouts per batter faced or strikeouts per inning. Granted, K/100P has vestiges of BB and BABIP mixed into the formula, but there are arguments against K/9 and K/BF as well.

I have written several articles on K/100P and summarized my findings here.

Strikeouts. Pitch totals. Putting strikeouts in the numerator and pitch totals in the denominator allows us to measure dominance and efficiency or what I have referred to it as "strikeout proficiency." As a standalone stat, I believe it tells us more than K/9 or K/BF.

With the foregoing as a backdrop, let's take a look at the K/100P results for 2009. The stats are courtesy of ESPN and the list includes all qualified pitchers. (I discuss some of the leaders and laggards below the table.)

Has Javier Vazquez been the best pitcher in baseball this year? One could certainly make a strong argument on his behalf. The 32-year-old righthander leads the majors in not only K/100P by a fairly wide margin but also in total strikeouts and K/9 and is second in WHIP and third in K/BB. His 4-6 W-L record belies just how well he has pitched this season. His FIP, in fact, is three-quarters of a run below his ERA.

If Vazquez hasn't been the top pitcher this year, then how about Dan Haren, who is eighth in K/100P but first in WHIP and K/BB? While the 28-year-old righty may be in the midst of a career year, there are a couple of stats (notably, a BABIP of .238 and a strand rate of 86.0%) that suggest his ERA may be unsustainably low. I picked Haren to win the Cy Young this year so I'm not overly surprised by his stellar season.

Tim Lincecum and Roy Halladay are building on their great seasons last year, while Zack Greinke (April and May) and Justin Verlander (late April to the middle of June) have been the most dominant pitchers in the game at various times over the first three months of the season.

Among the top dozen (or those with K/100P rates over 6.00), Jorge de la Rosa is probably the one pitcher who looks like he doesn't belong. While I would take the other 11 pitchers over him, I believe the 28-year-old southpaw is much better than his 3-7 W-L record and 5.85 ERA would indicate. He pitches in a tough ballpark and has been victimized by a high BABIP and a low strand rate. The young fireballer is not all that different from teammate Ubaldo Jimenez even though the latter has posted a 6-6 record with a 3.73 ERA thus far. If de la Rosa can improve his command and control (far from a given), he could eventually reach his vast potential.

At the other end of the spectrum, Jon Garland and Shairon Martis are pitching about as poorly as any regular starter in the big leagues. Both righthanders have not only struck out fewer batters per 100 pitches and inning than any other qualifier but they have allowed more walks than strikeouts, a recipe for disaster no matter what one's BB rate may be.

No Sgt. Pepper's Lonely Hearts Club Band, Led Zeppelin IV, Goodbye Yellow Brick Road, Saturday Night Fever, or Phantom of the Opera. But, my, what a Thriller it was to find out what I now owned. Included in the tapes (and now CDs) were two interviews of Don Drysdale and my father on the Angels Warm-Up Radio Show in 1974.

While Dad had interviewed the "Big D" dozens of times over the years as a beat reporter covering the Dodgers for the Long Beach Press-Telegram, the roles had been reversed and it was Drysdale, the play-by-play broadcaster for the California Angels, interviewing Dad, the team's Director of Public Relations and Promotions. Their careers had overlapped with the Dodgers and Angels like no others from the 1950s to the 1970s.

Drysdale made his MLB debut with the Brooklyn Dodgers in 1956 and was a budding star when Dad began to cover the club after owner Walter O'Malley had relocated the franchise to L.A. in 1958. The two of them stayed with the Dodgers throughout most of the 1960s with Dad and Drysdale both retiring from the Dodgers in 1969. Dad joined the Angels before that season started and worked for the organization for the next ten years. Drysdale hooked up with the Halos in 1973 through 1979 and returned for one year in 1981. (Interestingly, Drysdale's sidekick, Dick Enberg, broadcast Angels games from 1969-1978, matching Dad's tenure with the team exactly.)

The following Warm-Up shows took place 35 years ago. Nineteen years later to the day of the second interview, Drysdale died of a heart attack in his hotel room in Montreal during a Dodgers road trip. He began his career as a Dodger and died a Dodger. He was 56. Like Drysdale, my father passed away at a young age. Dad was 50 when he died of melanoma in 1978.

While I know these "lost" tapes mean more to my family and me than to the baseball public at large, I wanted to share them on the day after what would have been Dad's 81st birthday and the one before Father's Day. Oh, and isn't it fitting that the Dodgers and Angels are playing each other this weekend? My older brother Tom, in fact, went to the game last night.

Happy Father's Day to all the dads out there. Be sure to give your loved ones a kiss and a big hug on this special day. None of us know what tomorrow brings.

"Yesterday is history. Tomorrow is a mystery. Today is a gift. That's why it is called the present."

Thanks, Dad. And thanks, Mom, for the special Christmas gift. It's nice to have you in the present.

* * *

Don Drysdale Interviews George Lederer on the Angels Warm-Up Radio Show, 5/7/74:

Don Drysdale Interviews George Lederer on the Angels Warm-Up Radio Show, 7/3/74:

Max Marchi already looked at this topic using the GameDay hit location to determine the horizontal angle of the ball in play. He examined the tendency of hitters to pull inside pitches and go the other way with outside pitches. He also looked at the possibility of defensive realignment based on a given hitter's spray chart. Here I am going to look at the first topic and ignore the second which led to an, at times, heated discussion over at the Inside the Book blog.

In Max's work he looked at how much individual hitters pulled the ball based on the pitch location. Here I am going to average over all hitters to find a baseline. Below I show the horizontal angle of a batted ball based on the location of the pitch. Remember that negative angles correspond to to the left side of the field and positive to the right. In this case I chose a red-to-blue color scheme to high-light the difference between pulled and opposite field balls in play. I also flip the colors between RHBs and LHBs so that red is always pulled and blue opposite field. Like always the images are from the catcher's perspective.

Horizontal angle by pitch location

As expected inside pitches result in the furthest pulled balls and it is not until you get to the outside edge of the strike zone that the average ball in play is to the opposite field. So batters have a tendency to pull the ball, with a pitch down the middle on average being hit to about 5° to the pull side. In addition there is a slight trend for pitches low in the zone to be pulled more. It looks like RHBs pull the ball more than LHBs.

Horizontal angle by pitch location for ground balls versus balls in air

I was also interested in how strongly ground balls are pulled compared to balls in the air (fly balls, pop ups and line drives). Conventional wisdom is that ground balls are pulled more, as evidenced by the infield shifts that hitters like David Ortiz experience. In addition, Matt Lentzner set up a simple bat-ball collision model that predicted most ground balls go to the pull side and more balls in the air to the opposite field side.

So we have conventional wisdom and theory telling us what to expect, let's see what the data say. I redid the above analysis first with ground balls and then balls in the air. Instead of using the GameDay classification for GB versus LD or FB, I used the HITf/x vertical angle. Based on Harry Pavlidis' work here it looks like 7° is a rough cutoff between a ground ball and a ball in the air. So that is how I separated the batted balls.

Just as expected ground balls go to the pull side much more often than balls in the air. For about the inside two thirds of the plate the average ground balls goes at least 10° to the pull side. Again RHBs show a stronger tendency to hit to the pull field. This could be because infield hits are more likely to the left side of the infield than to the right, so RHBs have an incentive to pull ground balls while LHBs have an incentive to go the other way with ground balls.

Fly balls, pop ups, and line drives have a much smaller tendency to be pulled and again it is weaker in LHBs. In fact there is almost no pull trend for LHBs on balls in the air; they tend to pull inside pitches and go the other way with outside ones.

Speed of ball off bat by horizontal angle

Finally I was interested in how much additional power a pulled ball has than one hit the other way. Mike Fast showed that pulled balls are more likely to be home runes, more likely to be line drives and have higher BABIP than opposite field balls in play. In fact, Mike showed, a pulled fly ball is ten times more likely to a home rune than an opposite field fly ball. I wanted to see the difference in speed off the bat responsible for this huge effect. Here is the horizontal speed of the ball off the bat by horizontal angle for LHBs and RHBs.

Batted ball horizontal speed reaches a maximum roughly between 5 and 25 degrees to the pull direction. Pulled balls are roughly 10 to 20 mph faster than those hit in the same angle to the opposite field.

Of course all of this analysis averages over all hitters. We know there are hitters who are assumed to be 'dead-pull' hitters or those with power to all fields. The data are now there, in a small sample with more coming, to examine these classifications. Do such hitting syndromes exist? How consistent are they for an individual hitter year to year? How does it impact a hitter's performance? It will be very interesting when enough HITf/x data become available to look at individual hitters at this level.

Paul and I have remained friendly over the past four years. The Harvard two-sport athlete and cum laude graduate is as nice as he is competitive and smart. He is also a fellow blogger and perhaps the only senior member of a front office to operate a baseball-related website.

Now an Executive Vice President for the San Diego Padres, DePodesta has spent the past 13 years working with, for, and hiring some of the brightest minds in the game, including, among others, John Hart, Dan O’Dowd, Mark Shapiro, Josh Byrnes, Neal Huntington, Chris Antonetti, and Ben Cherington with the Cleveland Indians (1996-1998), Billy Beane, J.P. Ricciardi, and David Forst with the Oakland A's (1999-2003), Logan White, Kim Ng, and Dan Feinstein with the Dodgers (2004-2005), and Sandy Alderson, Kevin Towers, Grady Fuson, and Bill Gayton with the Padres (2006-2009).

DePodesta and winning are synonymous with one another. The Indians won the American League Central all three years, the A's won the AL West three times and finished second the other two seasons, the Dodgers captured the franchise's first division title since 1995 and first postseason berth since 1996, and the Padres won the NL West and missed tying for the division title and wild card spot by one game the following season. All in all, the clubs DePodesta has worked for have won eight division crowns and accumulated a won-lost record of 1,137-943 for a winning percentage of .547.

At 31, DePodesta was the third-youngest to become a big-league GM when Dodgers owner Frank McCourt made him his first hire on February 16, 2004. (Theo Epstein was 28 when named GM of the Red Sox in 2002 and Randy Smith was 29 when the Padres hired him in 1993. Jon Daniels subsequently became the youngest GM in baseball history when he ascended to the top spot with the Rangers in 2005 at the age of 28 years and 41 days.)

Paul is married and has two sons and a daughter. His wife Karen is a La Jolla High alum. He has enjoyed his tenure with the Padres for professional and personal reasons. Paul has also served as a keynote speaker at business conventions and his work was featured in Michael Lewis' best-selling book Moneyball: The Art of Winning an Unfair Game, and recognized by Fortune, which named him one of the Top 10 innovators under the age of 40.

I had the opportunity to chat with Paul shortly after the MLB Draft was completed last week. Pull up a chair and enjoy.

Rich: On your blog, It Might Be Dangerous... You Go First, you have a poll up, asking "How do you feel about the Padres draft?" The majority of the respondents have voted "Happy." How do you feel about it?

Paul: I'd say "ecstatic," but that's probably how most club officials feel right after their draft.

Rich: The Padres had not taken an outfielder with its first pick since 1999, yet drafted center fielders Donavan Tate (Cartersville HS, GA) and Everett Williams (McCallum HS, TX) in the first and second rounds, respectively. Both players are the sons of former NFL players. Did San Diego make a conscious effort to get more athletic in this year's draft?

Paul: It's the first time since I've been here that we drafted anywhere in the top 20, so we had a different type of player available to us this year and we wanted to take advantage of that opportunity. We've taken some other high school outfielders within the top 50 picks in recent drafts (Kyler Burke, Jaff Decker), so it wasn't necessarily a total departure for us. Due to the work of Grady Fuson and Bill Gayton over the last few years, we feel good about our farm system as a whole, so we really focused on the best player available in each round rather than worrying about organizational need.

Rich: Let's talk about Tate for a minute. Which players, past or present, serve as good comps?

Paul: A lot of different names have been thrown around. I know Donavan admires Grady Sizemore, and there are some similarities there. Some others would include a young Andruw Jones, Mike Cameron, or even Adam Jones.

Rich: Tate has signed a letter of intent to play baseball and football at the University of North Carolina. He is also represented by Scott Boras. Do you not feel as if he is going to be a difficult or costly sign?

Paul: Donavan has had a lot of options presented to him recently, including USC and Michigan football, UNC baseball and football, and now the Padres. Our feeling is that despite his impressive talents on the football field, Donavan's first love is baseball.

Rich: I remember Tate in the Aflac Classic last August quite well. He had a couple of memorable at-bats. In the sixth inning, after being behind in the count 0-and-1 and 1-and-2, he worked the count to 3-and-2 before flying out to left on the eighth pitch of his at-bat. Tate had an even better at-bat in the ninth inning when he fell behind 0-and-2 and fouled off five pitches on his way to working the count to 3-and-2, then got on base via an infield single to shortstop. He also scored the East's first run that inning on a wild pitch. I wrote down on my scoresheet, "tall, strong, fast, runs well" but was most impressed with his approach in those two at-bats.

Paul: It's interesting that you mention those ab's, because we heard some rumbings during the spring that Donavan's bat was "raw," and yet we had a representative at nearly all of his games and just didn't see it. I guess it all depends on the perspective: the bat may be "raw" as compared to his other tools or as compared to the top college bats, but as compared with the other high school bats... we felt comfortable with the risk.

Rich: Between Tate and Williams, which one do you see sticking in center field?

Paul: We think they're both good enough to play there. We hope to have that problem someday.

Rich: I saw Williams play in the Area Code Games last summer. After a terrific BP session, he went 0-for-5, striking out three times. It looked to me like he was having trouble handling breaking balls and lefthanders. Has he improved in these areas?

Paul: We see Everett as a pretty polished HS bat. That doesn't mean he won't need to make some adjustments - even the best big leaguers have to - but he has a natural feel for the barrel that is difficult to teach.

Rich: In the third round, you drafted Jerry Sullivan, a 6-4, 200-pound righthander out of Oral Roberts University. What do you see in him?

Paul: Jerry was a top prospect coming out of HS before having Tommy John surgery. Nevertheless, he came back strong from the procedure and had a stellar career at Oral Roberts. In addition to being an excellent athlete in a 6'4", 200 lb body, he has always thrown strikes with a fastball that ranges from 90-94 as well as a tough slider and solid change.

Rich: As you mentioned, Sullivan had Tommy John surgery while in high school. Do you believe that pitchers who have undergone elbow reconstruction surgery in the past pose lower injury risks than those who have not?

Paul: Not neccesarily. In fact, pitchers with prior arm injuries can be at greater risk going forward. However, we've learned by painful first-hand experience that every pitcher comes with significant risk.

Rich: I was surprised that Keyvius Sampson (Forest HS, FL) was still available in the fourth round. A three-sport star in high school, he was 93-94 and struck out the first two batters he faced (both of whom went in the top 35 in the draft) in his only inning of work in the Aflac Classic last year. At 6-foot-1, he is not as tall as some of the prep power pitchers who went in the first round, but it still seems like he was a steal as the 114th overall pick in the draft.

Paul: With the format of the draft this year (only three rounds the first day), we had ample time to digest the first three rounds and prepare for the next set of rounds. Since we were picking 3rd again on Day Two we were pleased to find a number of interesting players remaining on the board, and Keyvius was certainly one of them. As you noted, he’s a terrific athlete, and we like both his arm speed and his feel for the changeup.

Rich: How do you, Kevin Towers (EVP/GM), Grady Fuson (VP, Scouting & Player Development), and Bill Gayton (Director of Scouting) work together when it comes to the draft?

Paul: The four of us in addition to our cross-checkers, Scott Littlefield and Bob Filotei, discuss all of the top picks, and there's generally a consensus. At the end of the day, it's up to Grady and Chief to make the final call.

Rich: How much of your time do you spend on scouting?

Paul: I start entering draft mode around the end of February/beginning of March. Once the ML season begins, though, I spend probably 90% of my time on the draft until we announce that last pick.

Rich: Do you think the standard five tools (hitting for average, power, arm strength, fielding, and speed) are still the most important attributes of a player? Or would you insert plate discipline/pitch recognition skills into the mix?

Paul: Both tools and skills are important, as they often depend on one another in order to play. For instance, the combination of all tools and no skills is usually a promise unfulfilled, and all skills with no tools often results in a short career. We'd all prefer a plethora of both, but in the absence of that it's a constant effort to figure out if the shortcomings in one area will inhibit the positives in the other.

Rich: While I understand "we're not selling jeans here," what roles do height, weight, and body type play in assessing current and projectable talent?

Paul: You may be asking this because our draft class looks as much like a football or basketball team as it does a baseball team. Malcolm Gladwell once wrote that 3.9% of all adult males are 6'2" or taller, and yet 30% of Fortune 500 CEO's are 6'2" or taller. The fact is that people, in general, maintain an inherent physical appearance bias, and in sports we tend to gravitate toward big, strong guys. Therefore, nature pushes us to overvalue size at times, but things like strength, leverage, and angle can make a difference as long as there are underlying skills.

Rich: How do rank attitude, hustle, and leadership when scouting players? And how do you go about valuing those characteristics?

Paul: It can be really difficult for me to warm up to a player who has a low motor, but that's just my personal take and one that I often have to guard against when writing reports. I prefer guys who play with energy and appear to really enjoy being out there. The minor leagues can be a real grind - I can't imagine enduring that playing schedule - so I worry about guys who don't seem to have that passion. That said, that passion isn't always illuminated by a player bouncing around the diamond, which is why I have to be careful.

Rich: Is "feel for the game" something that is at all quantifiable? Is it inherent in most players or can it be taught or gained over time?

Paul: I don't have a good answer for that. Every player is unique, and sometimes we'll find a player who has terrific instincts for one part of the game while really struggling with other aspects of the game. Some of that "feel" though can come from experience.

Rich: How does ability vs. signability come into play when lining up your draft board?

Paul: We try to line up our board without accounting for signability. When it comes time to make a decision, we have to factor in everything we know, but we don't want signability to cloud our evaluations of a player's ability.

Rich: The Padres were just swept by the Angels over the weekend and are now 9-23 on the road this season. Small sample size, tough schedule, or is there something else at work here?

Paul: I wish I knew! You cite two potentially contributing factors, but it’s often tough to nail down a direct causal relationship. The reality is that winning consistently on the road is difficult, and if you can win just 50% of your road games you’ll probably be in contention for a playoff spot.

Rich: The club is now 14 games back of the Dodgers in the NL West. Given its place in the standings, is San Diego likely to be a "seller" prior to the trading deadline next month?

Paul: The ups and downs of this season have been easier to endure to some degree because we have a young club. Many of our players are still experiencing growing pains at the ML level, which is to be expected, and those aren’t guys we’re looking to trade. I believe we only have four free agent eligible players on our team right now.

Rich: When should Padres fans expect the team to be competing for division titles and playoff berths again?

Paul: One of the tough things about having a lot of young players is the unpredictability of it. Very few teams progress in a relatively linear fashion – 65 wins to 72 wins to 78 wins to 84 wins to 90 wins. Rather, it can look more like the “progression” of the Rays – 70 wins, 67 wins, 61 wins, 66 wins, and then 97 wins. Even the Oakland teams went from 67 to 78 to 65 to 74 before running off eight consecutive years of 87 or more wins. The point is that teams tend to change states rather dramatically much like water turning to ice. I will say that though we have patience to get to 32 degrees, we also have the highest of expectations internally.

Rich: I understand that you have a little bit of professional acting experience, having appeared in a few episodes of the TV show “Homicide, Life on the Street” back in the mid-1990s. As such, how do you feel about the casting choice of comedian Demetri Martin to play you in director Steven Soderbergh's adaptation of "Moneyball," which is scheduled for release in 2011?

Paul: He's a lot funnier than I am, but he definitely needs a haircut.

Rich: Good one, Paul. And with that, I'd like to thank you for taking the time to discuss the Padres, both present and future, with me today.

Paul: Happy to do it.

To see if these showers were just passing - maybe there was some way they would get it in - I went to the Chicago Tribune's mobile sports page to try and get some news on the game, the weather and what the chances the game would take place looked like. When I got there, I stumbled across this piece by Dan McGrath titled One opinion: Sox have better shot at playoffs than Cubs.

McGrath does not take a very analytical approach to the piece. Much of it is off the cuff and its intro makes light of how very underwhelming Chicago baseball has been this season. But nonetheless he does try and mount a case by breaking both teams down, comparing their competition and draws the conclusion that the Pale Hose look like a better bet for October than the Cubs. I disagree pretty strongly with McGrath's position on this one.

Let's just start with some facts. The Cubs are 30-30 and have scored 8 runs more than their opponents thus far in 2009. The White Sox are 30-34 and their opponents have outscored them by 27 runs. The Cubs are 2.5 games behind the San Francisco Giants in the NL Wild Card race and just 3 back of the Milwaukee Brewers for the NL Central. The White Sox are currently 4.5 out in the AL Central and trail the New York Yankees by 6.5 games for the AL Wild Card. While neither team has played the heavyweights from their respective leagues' East divisions (Red Sox and Yanks in Sox case, Phils and Mets for the Cubs), the Cubs have already played four games against the Los Angeles Dodgers, the class of the National League thus far. By a narrow margin, it would seem the White Sox have had the easier schedule to date and therefore figure to face a tougher slate down the stretch.

So the White Sox have a worse record, worse run differential, larger deficits to make up and need to do so against a tougher schedule. In and of themselves, these factors do not make conclusive the case that the Cubs are the more likely playoff bet but if you are going to say you like the White Sox' chances better, the onus falls on you to argue the position that much more persuasively. McGrath's defense in this respect falls way, way short.

Here is my favorite sentence, symptomatic of the rigor with which McGrath makes his case:

Even with Carlos Quentin ailing, the White Sox have enough guys having decent years that they've been impervious to teamwide slumps despite being shut out nine times.

Did you get that? They're impervious to slumps, but have been shut out nine times this season! Pick your garden variety crappy Major League Baseball offense. The A's? They've been shut out five times. Seattle can't hit, what about them? Again, five times. Sure the Giants must have been shut out a whole bunch? Three times. You get the point. It's hard to gloss over the fact that the Sox have been shut out nine times.

What's interesting is that the two teams have had a similar look thus far in 2009. They both pitch it very well while their respective offenses have slumped badly. This is in part thanks to injuries to star performers. Just as the White Sox badly miss slugger Carlos Quentin, the Cubbies can't get Aramis Ramirez back quickly enough. Did you know that with Mike Fontenot now playing mostly at third with Ramirez out, Aaron Miles is playing second and hitting .200/.240/.252! Maybe he remains on the Cardinals payroll?

The key difference between the Cubs and the Sox is that the former has a much better chance of seeing its offense improve dramatically. Geovany Soto, Alfonso Soriano and Milton Bradley all figure to hit much better the rest of the way than they have to date. Whatever the heck the Cubs decide to do at second base is going to be a lot better from here on out, too. Meanwhile, the Cubs do not have any clear regression candidates. Maybe Ryan Theriot just a bit? For the White Sox to improve on the offensive side, they first need Quentin back and a performing a lot closer to the way he did in 2008 than he was before he went down. Second, you need to place a lot of faith in guys like Chris Getz and Josh Fields and Brian Anderson, something that I find hard to do.

So to sum, the Cubs have a big leg up in the standings, have performed better to date and have better prospects to improve. It's why the various Baseball Prospectus playoff odds reports put their chances of making the playoffs anywhere between 25% and 35% or so. They're far from a slam dunk but they have a real shot. Those same measures have the White Sox around 5% to 10%, far worse than the Cubs' chances for all of the reasons I have described above.

This is a subject which has been covered before, over at Beyond the Boxscore, by Hardball Times' Victor Wang, and by other places. Here I intend to add to the discussion by adding a theoretical model to the mix to predict the lifetime win contributions from a particular draft pick. Obviously, it's no secret that the higher the pick is, the more production we can expect from a player, but just what is the difference between, say, the #1 and the #500 pick?

Baseball Reference recently has listed all draft picks in the history of the draft, which provides a handy reference from which to start this research. I collected all picks from #1 to #50 and then the picks from every 25th pick after that. This gave me a database of over 2,500 picks to analyze. I then matched this data with Sean Smith's lifetime Wins Above Replacement (WAR) values (due to data issues I actually used a home-brewed method of calculating WAR for very low achieving players - however the vast majority of WAR are from Sean's actual data).

WAR is probably the best metric out there for assessing a player's total value to major league teams, and so I use this as my statistic of interest. I use career WAR rather than WAR over the first six years (pre-free agency), although I think both are probably useful. Since I used career WAR, I had to either make some assumptions about the rest of recent players' careers or throw out a lot of data. I chose to impute the rest of recently drafted players' careers. I assumed that players drafted in 2001 had by now accumulated 50% of their lifetime win shares, gradually going back and increasing that amount to assume that the 1996 draft class had already earned 100% of their win shares. Draft classes 2002 and after were thrown out since it is too soon to predict a player's career win shares.

Fitting A Model

Looking at all data gives quite a messy picture. Of course there are many, many players at every pick clustered at the point where WAR equals zero. These players either succumbed to injury, flamed out, or otherwise never made it to the big show. A few players have slightly below zero values, meaning that they made it to the majors but performed so poorly that they played worse than a replacement player could. Then of course, there are the players with positive contributions, ranging from Barry Bonds' 174 Wins Above Replacement to Harold Baines' 40 WAR, down to the many, many Dave Clark's and Franklin Stubbs' who made a positive, but quite small contribution to their teams.

We can clean this data picture up, by plotting the average WAR at each draft pick, rather than plotting all possible data points. What we see is below:

As you can see, there is a lot of variability even when looking at the average WAR of each pick. However, you can also see that the data follows a definite curve. There is a major advantage to having the very first pick in the first round vs. having the last pick in the first round (#30 overall). The point where the expected WAR tends to level off also seems to be around the end of the first round of the draft. Mathematically we'd like to fit this curve to a model to get a theoretical valuation of each pick. The data certainly isn't linear, but instead seems to follow a definite power law and can be explained by the following formula:

WAR= a * (selection#)^ b, where a and b are the parameters of the model.

Running a non-linear regression, we find those parameters equal to a=19.8 and b=-.50. The model fits very well as you can see from the graph above.

What can we learn from it? Plugging the picks into the formula, we see that the #1 overall selection will accumulate an average of about 19.8 WAR over the course of his career. Meanwhile, it drops significantly to 14.0 WAR for the #2 pick. From there it drops rapidly to an expected 6.2 WAR for pick #10 before leveling off at 3.6 WAR for #30, 2.0 WAR for #100, and 0.9 WAR for #500. The model-based approach makes sense because it uses a relationship which both fits the data and matches our preconceived notions that the #1 pick is likely to become an excellent player, followed by a sharp drop-off in value with each successive pick until leveling off.

Other Factors Affecting Expected WAR

The beauty of a model is that we can also add other variables to the data to determine if other factors affect the curve. Going back to the full dataset (which gives the same parameter estimates as using the average by pick data), we can add terms to our model to differentiate between college players and high school players as well as between pitchers and hitters. The model was defined as the following:

WAR= (a + college*c + pitcher*p)* (selection#)^ b, where a and b are the usual parameters and c adds or subtracts to the scale parameter if the player is in college and p adjusts the scale parameter if the player is a pitcher.

We get the following results from our model. Others have talked about the wisdom of choosing hitters as well as college players and here we have a model that backs up this assertion. The results are below:

In formula form we get:
Expected Lifetime WAR = (20.7 + (-8.5 * pitcher) + (4.6 * college)) * selection ^ (-.49)

where pitcher is equal to 1 if a player is a pitcher, college is equal to 1 if he is a college player, and selection is equal to the # overall selection in the draft.

Here we see a major penalty in WAR for teams choosing a pitcher. If the player is a #1 selection, we would expect a difference of 8.5 WAR between a hitter and a pitcher. Meanwhile choosing a college player is indeed a benefit. The benefit of choosing a college player as the #1 pick amounts to about 4.6 WAR. Both of these numbers of course decrease in proportion to the power law as the draft goes on, so the difference between choosing a high school pitcher and a college pitcher is quite small in absolute terms by the time you get down to the 100th selection in the draft. Below is a pair of charts showing the expected WAR for each type of player at both the 1st and the 100th overall selection.

You can also take a look at a graph of each of the 4 types of players according to the model. As you can see, the shapes remain the same, with the hitters and college players having a higher expected WAR.

The model given above is just the final model with significant terms. I also tried using parameters for college players and pitchers in the exponent to see if the overall shape of the WAR curve changes depending on the type of player. However, this gave a null result, indicating that the pitchers, hitters, college players, and high schoolers all follow the same basic curve - just that hitters and college players start with a higher win expectation. An interaction term between the pitcher and college parameters also came up null, as did parameters distinguishing between various types of position players.

Conclusion

Overall, this analysis backs up the assertion made by others that college hitters have historically been best type of player to draft on draft day, meaning that sabermetrically minded teams can take advantage of this information (and some have been!). Of course, the more teams that catch on to this trend, the less advantageous taking hitters and college players will be. If all teams were drafting with an eye for maximum value with this information, all types of players would eventually have the same Expected WAR. However, I don't believe we are at that point yet.

Aside from measuring the effects of drafting pitchers and college players, this study is useful because it fits a nice smooth curve to easily quantify the expected WAR of each pick, allowing teams and fans to know what type of player to expect with each pick using a simple formula. Armed with this information, we can know what to realistically expect from the players recently selected on June 9th.