A lot of great stuff this week online from a baseball stat and sabermetric perspective. I don’t want to take too much time building it up, so I will just get right into the details.

The Great Clutch Project

I mentioned this a few weeks ago, concerning Tom Tango asking fans to join him in a battle in the clutch debate where he will pit his “good” hitters against the fans’ choice for “clutch hitters” to see if there really is a way people can see and perceive clutch. The stats he is using are his Leverage Index scores and wOBA (weighted on base average), so if you are not familiar with those, read up on them. You can find the summary of the project here, and Fangraphs will be running the season tally here for 2008.

Never thought I would see a Ginger/Mary-Anne and clutch/non-clutch analogy used, but I guess nothing should surprise me anymore.

Hardball Times OF Arms

John Walsh of THT reveals his new defensive metric to measure OF arms, something that has always been missing and that is sorely needed in the defense discussion that has escalated in the past few years. You can search by year on their stats page here, and there is a lengthy description of the methodology at this link. The stats for the OF arms goes back to 2004.

Richard Justice’s war with the stat guys

Local sports writer for the Houston Chronicle has the stats world up in arms from his reaction to a post by Mitchel Lichtman on Justice’s blog piece this week about bunting and how it is always a bad idea. Apparently, Justice has long been a target of some bloggers for his inability to look past emotion and personal feelings and look at the numbers. And the blogosphere just can’t get enough of all of this. And all of this from a couple of sentences about how bunting is always bad in the situation with which the Astros were faced.

For the record, I fall somewhere in the middle of what Lichtman and what Keith Law propose (you need to read all the threads to understand where that is). A manager has to make a yes/no decision in that moment, but his job should be to be as prepared as he possibly can with all the available data that will help him make an educated decision. The ones that are too close to call? Well, that’s why a managers are paid the way they are.

Lineup Analysis

This is not a new tool by any means, but something I have been messing around with this week that I recommend. Baseball Musings hosts a page that has a Lineup Analyzer put together by Morong, Arneson, and Armburst that allows you to put in any nine players with their OBP and SLG and it will construct the ideal lineup based on those numbers, and their calculated comparison and analysis of the two.

Here is the page, use it on your favorite team for this year or any year.

Using a relatively new tool on BaseballReference.com known as the Batting Order Outcomes, I thought it might be fun to go back and look at last season and construct the ultimate lineup, spots 1-8, using each team’s production in each of those spots as our data.

The way this page works is you can put in any team and any spot in the lineup (1-9) and BR will pull up a page with stats on how that team performed in that season at that spot in the lineup, with all PA included throughout the season.

So, I can quickly go back and see that in 1972, The Boston Red Sox had an OPS of .625 in the 7th spot, with the famous Doug Griffin getting the majority of the plate appearances that year.

Using OPS as our gauge, I will lay out the ultimate 2007 batting order from across the Majors. While the batting order page has incredible splits and breakouts of stats per month, player, inning, relative score, and more, the stats used are pretty basic, so OPS is probably our best bet for this exercise.

Starting with the leadoff position, here is the best from each spot in 2007, with a couple of my random comments associated with each:

1. Florida Marlins - .897 OPS

This one makes sense especially when you consider that Hanley Ramirez was given 706 of the 780 plate appearances for the Marlins in the leadoff spot in ‘07. May and June were actually not kind to Ramirez and the Marlins’ leadoff spot; the OPS totals for those two months in that spot were .738 and .694, respectively. But the next three months had totals of 1.094, .875 and .944 - so he certainly finished strong. In comparison, Ramirez’s two counterparts, Rollins in Philly and Reyes in NY, both contributed to .869 and .772 totals for their teams. Ramirez is expected to move to third in the order in 2008, so don’t look for the Fish to repeat in this spot.

2. St Louis Cardinals - .870 OPS

This one mildly surprised me. No Derek Jeter, Kevin Youkilis, Placido Polanco, or even Hunter Pence took this spot. Rather, the combination of Chris Duncan and Rick Ankiel give the Cardinals the top spot. Certainly helping the cause, Ankiel slugged .603 batting second. Also contributing to the solid .870 number were the OPS numbers by Scott Speizio and Skip Schmaker, who both had an OPS over 1.000 in 131 total plate appearances.

3. Boston Red Sox - 1.034 OPS

No surprises here. David Ortiz ate up 89% of the 751 total plate appearances in the third spot. I have heard some people say that Ortiz had a down year last year because his homeruns and RBI were down from the previous two seasons, but that argument is truly ridiculous. His batting average, OBP, OPS+, Runs Created, and Runs Above Replacement were all the best of his career. His 52 doubles made up for “only” 35 HRs - a number which will likely trend upward in 2008. And in September, during the playoff push, Ortiz’s OPS was a mere 1.355.

4. New York Yankees - 1.069 OPS

Again, no surprises at this spot. Of 744 2007 plate appearances in the #4 spot, A-Rod had 700 of them, with OPS of 1.081. In the few times someone else actually hit in this spot, Jorge Posada, Miguel Cairo and Hideki Matsui all had an OPS of at least 1.000 as well. And quite possibly even more impressive, the Yankees who had the number four spot come up with RISP 243 times, totaled an OPS of 1.127.

5. Toronto Blue Jays - .939 OPS

I probably could have given you a dozen guesses to this one, and you wouldn’t have said the Blue Jays. But there they are - with big Frank Thomas leading the way with his .935 OPS. Actually, while Thomas had the most PAs in that spot, he only accounted for about a third of the total plate appearances. Some of the other notable names hitting in that spot: Aaron Hill, Troy Glaus and Matt Stairs totaled OPS scores of .946, 1.145 and 1.003, respectively. All of these numbers represent significant increases over their seasonal totals.

6. Colorado Rockies - .908 OPS

This spot makes sense as well, with Brad Hawpe demanding 73% of the PAs for the Rockies in 2007. And while Hawpe’s OPS in 2007 in that spot was an incredible .918, it is severely overshadowed by Ryan Spilborghs who had an OPS of 1.212 over 74 PAs in the six hole. In another interesting note, the Rockies only had one month all season (April) where they did not slug at least .500 from the 6th spot in the lineup. Perhaps not surprisingly, that was the month they had a losing record.

7. Philadelphia Phillies - .850 OPS

This spot in the Phillies’ lineup was distributed pretty evenly amongst Abraham Nunez, Jayson Werth, Wes Helms, Greg Dobbs, and Aaron Rowand. Except for Nunez, all other batters had an OPS of at least .847 in the seven spot, with Rowand leading the way a 1.070 over 87 plate appearances. One entertaining and interesting note here looks at when throughout the course of the game the Phillies really produced in the 7th spot. In the 1st-6th innings, the Phillies had an OPS of .885 in the seventh spot, but that number drops to .783 from the 7th-9th innings.

8. Pittsburgh Pirates - .800 OPS

I could probably give you 25 guesses and you would not have picked the Pirates in this spot. I certainly thought it would be Robinson Cano or some other powerhouse offense, not the team that was 12th in the National League in runs scored. But with Jack Wilson and his .825 OPS getting exactly half of the plate appearances, the Cesar Izturis’s, Jose Castillos and Jose Bautistas of the world could not drag down the total number below .800. The second half of 2007 is what tells the story for the Pirates earning this spot - as a team the OPS in the 8th spot after the All-Star Break was an amazing .899.

In an exercise like this, the Magglio Ordonezes, Matt Hollidays and Miguel Cabreras unfortunately get stuck on the outside. But I certainly think that a team composed of this lineup would score an astonishing amount of runs. But just how many? Well, using the basic Runs Created formula, we can come up with a good guess as to just how many.

Formula: ((H+BB)*(1B+(2*2B)+(3*3B)+(4*HR)))/(AB+BB)

Total estimated Runs Created: 1024

In context, the team with the most runs in 2007 were the Yankees with 968 and the average across MLB was 777.

So in other words, we have quite an offensive machine on our hands, even including batters from the Pirates, Cardinals and Blue Jays.

There are not many things more beautiful to me than a ballpark open for the first time in the spring, or a perfectly executed hit and run, or a majestic homerun that clears a park. I can always find beauty in the simplest of forms at a baseball game, and there are not many things that rival what I see at the park.

But something that tops everything on that list is my new baby girl.

I have taken the past eight weeks off from doing something I love that in the end means nothing to spend time with someone I now adore and that now means everything. Now that things are starting to get back to a normal schedule (or as normal as it will be), I hope to be able to pick back up where I left off and get back to some research.

While I will never doubt the beauty of my new daughter, beauty on the baseball field or in the box score is something that has been debated for more than a century. Specifically with statistics, as we have seen in the past, the naked eye can often lie when it comes to observing and, in turn, trying to qualify a “good” player. Everyone knows the old quote from Bull Durham about the difference between a .250 hitter and a .300 hitter:

“…one extra flare a week, a ground ball, a dying quail… you’re in Yankee Stadium.”

Essentially, it’s VERY hard to tell between a mediocre, .250 hitter and a great .300 hitter. So when fans, announcers, managers, or anyone make general statements about how hitters perform based on what they see or what they believe, it’s always best to take it with a grain of salt.

A situation like this came up towards the end of the 2007 regular season as I was watching an Astros/Brewers game in late September.

In a game that featured two of the Majors’ top rookies for the season, the announcers on Fox Sports began discussing the value that Hunter Pence and Ryan Braun had on their teams this past year. In noting that both of them had very good batting averages (Braun finished the year at .324, Pence at .322) a comment was made along the lines of “rookies will typically hit for a higher average when they arrive in the majors because the quality of the pitchers is much better in the majors and they are able to be around the plate much more than their minor league counterparts.”

I don’t have the transcript of the game in my possession, so please don’t take that word for word, but the general idea is there. That because hitters see more hittable pitches when they come to the majors, they will be better hitters when it comes to average.

So I immediately thought, can this be true? Never mind that pitchers in the majors hit their spots better and their fastballs are faster and their breaking balls have more movement. And forget that defenses are better, travel is more brutal, and playing time for rookies is usually more sporadic; does that actually translate into better stats for rookies when they are facing tougher competition? That got me thinking about 2007 and using it as a case study for rookie production in the majors vs. their minor league numbers.

These broadcasters did not qualify their statement by specifying any level of the minor leagues, so it is pretty easy to pull a list of rookies and their 2007 MLB batting averages and compare them to their minor league career averages. I chose rookies with at least 150 plate appearances so we could see hitters who at least had routine/daily at bats. Here is the list of the 55 who qualified (actually there were 56, but Akinori Iwamura has no minor league stats to work with) ranked in order of their 2007 MLB batting average:

A simple count of these rookies shows that only 14 out of 55 (or 25%) out-performed their career minor league batting averages in their first major league season. And out of those 14, four of them beat their minor league total by .005 or less. Running a simple correlation of the two sets of numbers shows that the two sides (minors and MLB 2007) are not statistically significant (with r=.191 and p=.162). Simply speaking, looking at a player’s minor league average before 2007 would not be a good way to predict or even estimate their batting averages as a major leaguer in 2007.

You will always have your studs coming out of the minors who find a way to translate that talent into almost instant success in the majors such as Ryan Braun, Hunter Pence, and Troy Tulowitzki. But does everyone remember all of the experts’ preseason Rookie of the Year, Kansas City’s Alex Gordon? He was actually being hailed as the next Mike Schmidt. But after a few benchings and a .247 average on the year, he did not receive a single vote in the category. And what about Justin Upton, Elijah Dukes, and others who were supposed to pay immediate dividends? There are plenty just like them who did not pan out as originally advertised. And not to say Gordon won’t become Schmidt….just not this year.

So, if average is not a good predictor of success from the minors to the majors, what might be? We need to look at a more cumulative offensive statistic, not just one that says, “I got this many hits in this many at-bats.”

What I want to propose is Runs Created per Game or RC/27. We are all pretty familiar with the stat Runs Created. It simply takes into account a player’s offensive production based on runs he created for himself and for others on his team and tallies it into a calculable, sum total. What RC/27 does is ask the question, “what if there was a whole lineup of X player? How many runs would that lineup score per game?” For example, in 2007, the top three in the category were David Ortiz (surprisingly first at 10.86 runs/game), Alex Rodriguez (10.49), and Magglio Ordonez (10.12). That tells you how good these guys were - can you imagine a team that would average more than 10 runs per game? The Yankees had the highest average in 2007 with 5.83 runs per game (and their best month was September at 6.67).

Anyway, RC/27 will take into account not only the runs created by the batter by themselves as well as opportunities presented to that player by teammates and how he performed in those circumstances. Using the same 55 players, here is the list of their career minor league RC/27 numbers vs. their numbers in their rookies seasons of 2007:

Running the correlation again, we see that the numbers for RC/27 comparing minors to 2007 MLB ARE statistically significant (r=.268 and p=.05). So while not perfect, Runs Created per Game would be a much more reliable stat to judge performance across levels of competition.

My guess is that this would be partially due to the fact that a player’s pure talent should eventually translate across the levels he plays in, whether good or bad, in looking at how he performs on offense individually. Average only accounts for one piece of the offensive puzzle: how many times did I get a hit in my times at bat? It doesn’t account for walks, what type of hit it was, who was on base, whether they got the hit with one out or two outs, etc.

Another theory of mine is that in the majors, these rookies will be playing and batting in a lineup of players that (should) actually belong in the majors. I imagine that would lead to more consistent opportunities of plate appearances with men on base, men in scoring position, and also competent hitters batting behind them, allowing something like RC/27 to stabilize quicker with less variance than something like average where it is solely reliant upon batter and pitcher; one at bat. But, then again, that’s just my opinion, and the topic of a whole other post with different numbers to crunch.

Unfortunately, this is a difficult study to continue to quantify. The statement proposed by the announcers about the averages in their rookie seasons qualifies the research and limits the set of data we can use for the players. Once their second year comes around, they are not rookies anymore and their MLB numbers can’t be used anymore.

But if someone wanted to take on the task of comparing the numbers from say 1986 to 2006 for rookies and see how they correlate, I would be very interested to see it. Would average then become significant over 20 years? Would RC/27 become less so? I would be curious to know.

Just be sure to always question what you hear if it doesn’t sound right to you. There’s a good chance it’s not based on facts.

And welcome back to Baseball Notes. More to come soon…

Since the first Baseball Abstract was published some 30 years ago, Bill James has been labeled a lot of things: revolutionary, heretic, genius, fraud, etc. His analysis and research has been praised and trashed at the same time. His work has been studied and acutely used by some big league clubs, and laughed at by others.

He has been credited for starting the suddenly not-so-underground sabermetric style of baseball analysis, and accused of reducing players and games down to mere data, not taking into account a manager’s keen eye or a player’s “makeup.”

Some of his research has even been labeled as unfounded, unusable and as something with no data to back it up. One of these innovations is the defensive spectrum.

Quite simply, it is a spectrum drawn out from all defensive positions ranging in order of the least difficult to play to the most difficult. The idea is that a player can easily go from right to left on the spectrum as he gets older and some of his speed diminishes, but it is much harder to go from left to right, no matter what point of his career the player is in. It looks like this:

1B - LF - RF - 3B - CF - 2B - SS - C

(We will leave pitchers out of the equation since this will inevitably be related to offense, but they typically fall on the far right)

With no easy way to compare defensive difficulty across position, this spectrum looks pretty good at first glance. Obviously the skill set of a catcher is much more rare than that of a second baseman, which is much more rare than what a first baseman can provide.

Also associated with this spectrum, James said, is an expected offensive production. As you move more to the left on the chart, and you are not expected to provide as much defensively, you better be carrying your weight offensively. And that makes sense at first glance. Any baseball fan or Rotisserie player can tell you, your sluggers tend to be your first basemen, outfielders, and third basemen, while you see very few guys who play up the middle slug 40 homers or accumulate an OPS of 1.000.

What I am going to look at is have offensive expectations changed since this spectrum was first introduced in the 1980s? Or, more precisely, how accurate is the assumption that just as catchers offer more defensively than shortstops who offer more then second baseman, etc., first basemen offer more offensively than left fielders who offer more than right fielders, etc.

You see, while we can look at metrics such as zone rating and Fielding Runs Above Average, there is no way to compare specific, defined skill sets like blocking wild pitches or turning the double play or routes taken to flyballs across positions. But with offense, we can equally compare contributions. We know the sole objective of offense, which is to create runs for your team and do this at a better rate than the other team. That is the responsibility of every player whether they are a catcher, DH, right fielder, or whatever. And, yes, there may be a number of different ways to accomplish that goal of pushing runs across the plate, but the eventual desired outcome is always the same: score runs.

So, for 2007, let’s see how close James’ spectrum is to predicting its reciprocal: offense.

From what I can tell, a little bit of research has been done on this before a few years ago. This link takes you to a post where Mike Mehl used the OPS of the top players in the 2003 season to plot the offensive range by position. His conclusions were that the majority of your low OBP, low SLG batters fall on the right side of the spectrum, with only a few exceptions. And most of the batters on the opposite side had high OPS, although admittedly skewed by the Barry Bonds’ of the world.

For this post, I am going to use Runs Created and VORP as the comparative metrics for offense. Runs Created because it is an easy way to compare apples to apples. A run is a run is a run. Did you create a lot of runs or did you not? And VORP because it compares positions to themselves. There are stated, specific offensive factors when discussing VORP for each defensive position and its relative value of replacement level. All OF positions are weighed the same in VORP, but that will suffice for our exercise.

What I have done is taken the average number of Runs Created and VORP for the top 30 in each of the eight listed positions of the spectrum. I then charted it on a graph in the order of the positions on the spectrum. So, if the theory is correct (at least for this year), we can expect to see two lines that go from the upper left corner of the graph to the bottom right. Below is the graph and the numbers represented with each. Click on the graph link to show the full image.

rc-and-vorp-2007-positions.jpg

So what do we notice here with these numbers? Generally the line we would expect is there, but with a few interesting variations to point out:

1. Shortstops - While this position is still generally considered to be one where you have to have a decent glove to play, you can see that the average RC and VORP for SS in 2007 has now reached 3B and CF levels. Someday, I will do this exercise again for 1987, 1967, and 1947, for example, to see how the numbers compare. But the facts are that there are only 31 players in MLB with over 100 Runs Created, and four of them are shortstops (Ramirez, Reyes, Jeter, and Rollins). These four carry the group, but you still have Guillen, Cabrera, Young, and Renteria with more than 85. And another nine have 70 or more RC. The total of SS over 70 RC is 17, or more than half of MLB. Clearly, this position is becoming one where both defense and offense are valued. But I bet if you polled GMs across the league, they would pick great defense if they were forced to pick one quality their SS had.

Unfortunately, only one of these top eight SS (Reyes at #2) is in the top nine for Revised Zone Rating for 2007. Jeter, Ramirez, and Guillen are all in the bottom 10. So it’s still tough to find a SS who does both well.

2. Right field - RF in 2007 has been the Magglio Ordonez and Vladimir Guerrero show. Can you name a player who is clearly the third best RF this year? It’s not easy. In fact, there is a 26 point gap in VORP between second place (Guerrero - 62) and third place (Corey Hart - 36.9). The same goes for RC: there is a 25 point gap between Guerrero (123) and Abreu in third (98). In fact, there are only eight RF with more than 90 RC while there are seven SS!

But is this a growing trend? Looking back, there have not been more than eight RF with more than 90 RC in a season since at least 2003. Looking at our graph, RF VORP is right where it should be based on those numbers, while Magglio’s astounding 144 RC skews that plot line north a little bit.

3. Catchers - Obviously, you see how wide the gap is between your average catchers and the rest of the seven positions. There are only three catchers (Martinez, Posada, and Martin) with more than 70 RC so far this season. And the same three catchers are the only ones at their position with VORPs over 27 for the season. I actually count eight starting catchers with a negative VORP, meaning whomever the team could call up from Triple-A would probably be better offensively.

While the defensive spectrum is nonscientific and purely speculation based on perceived defensive attributes and responsibilities, it does seem to serve the purpose of evaluating where a player can move on the diamond as his skills diminish. It also seems to prove for 2007, except for the recent outlier of shortstops, that players are increasing their offensive skills as they move down the spectrum.

If you know of any examples of players moving in the opposite direction of the spectrum as their careers moved on, let me know. I would be interested to study them and see why that was. Of course, the most recent famous example of the spectrum at work is Craig Biggio who moved from catcher to second base to center field to left field in his career. Now back at second because of lack of options for the Astros, I hear he is going to play all of his old positions in his final game on September 30.

On September 11, I attended the Astros-Cubs game here in Houston that the Astros won 5-4 in 11 innings thanks to a Luke Scott walk-off triple off of Ryan Dempster. During games, as is my custom, I will usually try to pick up on some interesting trend, or notice something I have never seen before, or try to predict pitches, etc. You know, nerdy stuff. Well, something caught my attention that day and I have been thinking about it since.

Perhaps it is because I have Carlos Lee in my most competitive fantasy league, but I was noticing that night how many times he led off an inning. So when I got home, I checked the play-by-play, and sure enough, he led off an inning four times in his five at bats that night. In fact, the only time at bat where he did not lead off an inning was in the first inning. As a cleanup hitter, you would think that his first at bat would have been the most logical to lead off an inning if the first three batters were sat down in order in the first inning. But just like I thought, he led off the 4th, 6th, 8th, and 11th. One of the reasons this caught my attention was because a caller on a local radio show called about this same topic a few weeks ago and it stuck with me. But, not wanting to trust what that guy said or just my observation from Tuesday night, I went to check the numbers.

Carlos Lee has led off an inning 142 times this season including the Sept. 11 game. Is that a lot? A little? Somewhere in between? Obviously, leadoff hitters will have a lot more times leading off an inning because they do it every game. Number 8 and 9 hitters will do so much less frequently because they have so many fewer plate appearances than the guys in the top of the order. So Carlos Lee needs some peers to compare him to. And since inevitably I will quit rambling and look at how these many times leading off an inning led to decreased numbers of opportunities with men on base, I figured I would compare him to his fellow run-producers. I have included a list of the top 31 RBI leaders in the majors as of the Sept. 11 games (that is to say, everyone who had at least 90 RBI by that point). And, yes, I am aware that RBI is always a flawed stat to use to compare players, but since this ties in directly with the conclusion of my previous post where it was found that plate appearances with men on base, and not average or OBP with men on base, has a higher correlation with strong RBI numbers, it seems like an appropriate list to use.

Here is the list, with their homeruns and OPS when leading off an inning included:

As you can see, Carlos Lee is tied for third when counting both leagues and comes in first overall in the NL for number of times leading off an inning. Other interesting trends here:

1. As might be expected, the highest numbers of those leading off innings come from cleanup hitters like Lee, Morneau, A-Rod, Ordonez, Martinez, etc. Conversely, the lowest numbers are held by hitters in the third spot such as Utley, Hafner, Ortiz, etc. This makes sense because three-hole hitters are guaranteed that they will have at least one at bat per game where they do not lead off.

2. Of these 31 players, 16 have an OPS higher than their overall number when they lead off an inning, and 15 have a number lower. So in this small group of statistical data, no definitive conclusion can be drawn using OPS that this elite group of hitters are any more clutch when they come up with men on base as opposed to no one on. In this exercise, you know they are coming up with no men on in the plate appearances.

3. The four guys that typically hit in front of A-Rod, Morneau, Lee, and Wright are Bobby Abreu, Torii Hunter, Lance Berkman, and Luis Castillo (since he was traded), respectively. Of those four, you can definitely argue that three of them had significant portions of their seasons where they struggled or were in slumps (Berkman, Abreu, and Castillo). Could it be that the leaders in PA while LOI simply just fell into that because pitchers challenged the guys in front of them more frequently because they did not want to face these sluggers with men on base?

But getting back to Lee for a second, how do the numbers represented above affect Lee’s personal season statistics, or the Astros’ numbers for that matter?

According to Baseball Prospectus, there are only eight major leaguers who have had more PAs this season with runners on base than Carlos Lee. This surprising stat comes while Lee plays for a team that is 27th out of 30 teams in runs scored this season and a team that is 23rd in OBP. The other eight players ahead of Lee all play on teams that are in the top eight in the majors in runs scored. And if my addition is correct, only Alex Rodriguez has more plate appearances leading off an inning plus PAs with runners on than Carlos Lee.

What does all of this tell us? Nothing, really. Just that Carlos Lee has had some absolutely fabulous bad luck this year, his first as an Astro. Lance Berkman has gone from from a Hall of Fame type year in 2006 to merely just a good year in 2007, which apparently has included plenty of rally-killing third outs. Nothing could have helped the Astros this year - not even 142 more plate appearances for Lee with men on base. Lee would have to have created 120 more runs to account for 12 more wins, the number of games out of first the Astros currently find themselves.

But to have some fun with the numbers, we can certainly do some predicting here. Here is what we will use:

1. Maybe not so surprisingly, only Vladimir Guerrero has driven in a greater percentage of his team’s runs than Carlos Lee at 16.8%

2. In 2007, 50.3% of Lee’s PAs have come with men on base.

3. Lee has an OBI% (others batted in) of 17.5% - around 35th in the majors for qualified batters.

4. Lee’s breakdown of runners on each base per PA with men on and his percentage of driving runners in from those bases looks like this:

1B - 74% of PAs with men on - drives them in at a rate of 6.8%
2B - 43% of PAs with men on - drives them in at a rate of 18.2%
3B - 23% of PAs with men on - drives them in at a rate of 50.7%

5. The mean number of PA leading off an inning of the 31 listed above is 115.

So, let’s give Lee 115 PAs leading off an inning instead of 142. This gives him an extra 27 PAs throughout the season. Based on the the 50.3% from above, 14 of those will now come with men on.

Using the other percentages, 10 PAs will come with men on first and he will drive in one of them. Six of those PAs will come with men on second, and he will drive in one of them. Four of the PAs will come with men on third, and he will drive in two of them.

In the end, if we make Carlos Lee the average slugger/run producer, we only add four more RBI to his Sept. 11 total, and he now has 109.

When you have fellow batters like Lance Berkman in front of you all season who are constantly making inning-ending outs, there is not much you can do to change your luck. A lot was made here in Houston at the beginning of the season about having such a great tandem of 3-4 hitters in Berkman and Lee. But hindsight and Berkman’s “down” year and OBI% of only 16.3% shows that the Astros perhaps could have done a bit more damage with those two in the opposite spots in the lineup. But, again, it would have taken a lot more than “a bit more damage” to save the Astros’ season.

__________

Of course, as I write this, Lee just led off an inning with a homerun for the Astros on Thursday night.

In 2003, the Michael Lewis book, Moneyball, opened the eyes of a lot of baseball fans, insiders, and analysts as Lewis described the thought processes of GM Billy Beane and the 2002 Oakland Athletics in their quest to remain competitive in the American League despite a payroll significantly less than the Yankees, Red Sox, and Angels of the world. By valuing offensive traits such as on base percentage, slugging percentage, and walk rate, Beane was able to identify players that could be had at a tremendous value to the team through the draft, trades, and free agency. Using this philosophy of valuing skills many teams overlook (Moneyball emphasizes runners moving from station to station via hits, walks, etc; getting on base is valued more and expected more than moving runners along at the expense of outs), Oakland was able to make the playoffs every year from 2000-2003 and again in 2006.

Fast forward to 2005, and using a variation of smallball (sort of the anti-moneyball) called Ozzieball, the Chicago White Sox swept the Houston Astros to win the World Series. In smallball, teams use the hit-and-run, stolen bases, sacrifice bunts and hits, productive outs, and runner advancement to score runs whenever possible, and rely on quality pitching to always keep their teams in games. The 2005 White Sox finished with the best record in baseball, and a team 3.61 ERA on their way to the title.

Needless to say, the debate over which style of play is better has just begun - with countless numbers joining each side. Or is having a little bit of both the best? In 2007, teams such as the Boston Red Sox, Oakland Athletics, and the Cincinnati Reds continue to use the Moneyball philosophy, while others such as the Los Angeles Angels, St. Louis Cardinals, and Seattle Mariners rely less on the homerun and extra-base hit and move runners along however they can.

Looking at 2007, the White Sox are now one of the worst teams in baseball and the Athletics are struggling to reach .500 in a division that has had two teams run away from them. So while both teams that experienced such great success with their respective style of play struggle this season (something that was probably expected due to natural regression, especially for the White Sox), other teams using the different styles of play have risen up to take their places and are primed for the postseason.

So I thought an interesting study might be to look at 2007 offenses (pitching at another time) and see which teams are using the two separate paths, which teams utilize some of both, and how those correlate with winning percentages across Major League Baseball.

Then the question becomes how do you define teams using Moneyball, and how do you define teams using Smallball?

On the ESPN website, Rob Neyer concocted a fun little tool called the Beane Count (after Billy Beane) to tell which teams are playing the Moneyball style using hitting and pitching numbers that are reflective of that philosophy. Using reverse rotisserie style (the team in first place in a certain category gets one point, the last team in the NL would get 16 points, for example), he uses total HR and total BB for team offenses, and total HR allowed and total BB walks allowed for team pitching staffs to rate teams who at least unintentionally use Moneyball tactics the most. As of Sept. 3, Boston, Cleveland, and Oakland lead the AL, while San Diego, Colorado, and Cincinnati lead the NL.

What I tried to do was take that Beane Count concept and expand it a little bit, run the data for only offenses, and create an alternate set of comparative statistics for smallball stats. What I ended up with was a set of data that I call Money Count which ranks each team’s HR, BB, OBP, and ISO (Isolated Power; SLG-AVG); and Small Count which ranks each team’s Stolen Bases, Sacrifice Hits, AVG, and POP (productive out percentage; productive outs being any out that advances a runner with the first out or scores a run with the second out). I then ranked each team’s offense by these four categories the same way Neyer does in Beane Count, reverse rotisserie style.

The results for the separate spreadsheets are below (click on the link to enlarge, numbers are as of 9/2):

money-count.jpg

small-count.jpg

And here is the chart with each team’s winning percentage as of September 2:

From the two charts comparing moneyball to smallball, you see a few expected and a few unexpected things. In Money Count, slugging, slower teams like the Yankees, Red Sox, Reds, Indians, and A’s all show up in the top 12 or so. Similarly, faster, less powerful teams such as the Angels, Mariners, Cardinals, and Dodgers all show up in the top 12 of the Small Count chart.

What is interesting to see is teams where it is apparent that they have a combination of both sets of offensive traits. Teams such as the Phillies (with Howard, Rollins, Utley, Burrell, Victorino, et al), the Mets (Reyes, Wright, Beltran, Delgado, etc.), and the Devil Rays (Crawford, Pena, Upton, Young, Iwamura) have all developed players and qualities of being able to succeed offensively on both sides of the debate. They can beat teams with speed, out production, and the one-run inning; or they can mash the ball, reach base in a number of ways, and pile on the runs.

So looking at these two charts and then looking at team’s winning percentages. Can we compare the two and expect them to give us a significant correlation towards winning? Can we look at just 2007 offensive numbers and ask them to tell us how teams win or which way will contribute more towards a team winning? Offensive numbers from just 2007 is still a small sample size even when you are talking about five months worth of data - so a model built around 10 or 20 years of data would be better, but not having access to a database like that has limited me to 2007.

With the help of the brilliant Dr. Jared Benge, we ran multiple statistical tests to try and prove or disprove the theory that moneyball or smallball can correlate to or be a predictor of wins and winning percentage. What we found looks like this:

Trying to directly correlate moneyball style or smallball style to winning percentage proved difficult. Neither philosophy proved to be statistically significant based on 2007 numbers, but with the goal being p<=.05, Moneyball came in at .149 while Smallball came in at .912. Again, not enough data to make it significant, but Moneyball clearly comes closer to the goal of correlating to winning percentage.

So what we decided to do next was run a regression model to try and determine the explained variance of each of these philosophies to winning. The way it was explained to me was to think of a win or winning like a whole pie. By running a regression test for each style of play, we should be able to see what percentage of the variance or outcome (read:win) comes from each style. In doing this, we found that Moneybal received a variance of .073 or 7%. Smallball’s was essentially zero. The conclusion from this is that using this small sample size, 7% of a win can be explained by Moneyball, while Smallball essentially can not explain any part of it.

The bottom line is that regression is used to try and determine how much a predictor (the two different styles in this case) predict an outcome (which we want to make winning percentage). Trying to use either of these sets of data to predict winning percentage proves to be futile, and we see very little significance from Moneyball and even less from Smallball.

Our hypthesis is that over 10 or 20 years, if these numbers remained somewhat constant, Moneyball would eventually become statistically significant in determining a higher variance for winning percentage, or in predicting wins for a team. I will save that project for another day, but if someone wants to undertake the legwork, please feel free.

But in looking at individual teams, a pattern begins to show when comparing teams that populate high spots in both sets of rankings. Some of the best teams in the majors (Philadelphia, New York Yankees and Mets, Detroit, etc.) seem to have grasped an overall offensive approach: one that serves them well when needing to play a specific type of game that the circumstances demand.

So the debate will continue to go on until further proof can be produced. Perhaps someday we will undertake that. Someday we will collect all the data, modify our Count charts to more exact specifications, and run more tests. But for this year, the slightest of edges is given to Moneyball. For whatever that’s worth.

__________

If you want the full set of tests run for this project, let me know and I can find a way to get them to you. RK

From Yahoo! Sports:

Aug 7 Jim Street, of MLB.com, reports Seattle Mariners 1B Richie Sexson has been benched indefinitely by Mariners manager John McLaren. He has been replaced in the lineup by Ben Broussard.

Well, what do you know? It’s probably about time, don’t you think? In the midst of one of the worst seasons by a full-time player in recent memory, Richie Sexson is finally being benched in favor of Ben Broussard and others to fill in for the first baseman. While morons like me, the media, fantasy experts, et al have been waiting for Sexson’s “annual” second-half surge for some time now, many people, such as the good folks at USS Mariner, have been calling for this change for quite a while.

For a player who has a career .264 average and has had seasons of 45, 45, 39, 34, and 31 homeruns and 125, 124, 121, and 116 RBI and SLG% of .592, .578, .559, and .547, this 2007 campaign has been a complete disaster. With a line of .199/.298/.390 for the year, Sexson has been a complete bust since day one - and by far the worst every day first baseman in the game. Personally, I feel as though his struggles should be more magnified because the Mariners are right in the middle of a Wild Card chase, presently one game behind Detroit.

So that is what I want to look at today. What would the impact have been on the Mariners if Broussard had been the first sacker from day one? Would the Mariners be in better shape? Would they be in the Wild Card lead? How big would their lead be?

First let’s look at some numbers for our test subject. Where applicable, I have listed how poorly the numbers rank amongst his peers, whether first basemen or all of baseball. All numbers are as of August 7, 2007:

This is ugliness on a screen, my friends. Look at all of those categories where he ranks last at his position - and how unfavorable he compares to other major leaguers as well. In almost every rate stat that means anything, Sexson is dead last at his position and in the bottom 15% of the majors.

So what are the Mariners to do? Well we often talk about the replacement level in this space, so, using the same stats, let’s take a look at Sexson’s primary replacement, Ben Broussard, and his numbers on the season. Again, as of August 7:

Kind of funny that the replacement player’s stats are all better than the starter. With these stats, we can start to play a little game of “what if,” and see what might be different if the Mariners had played Broussard instead of Sexson this year.

Let’s assume these 564 plate appearances represent the sum total of the Mariners’ first basemen this year. I am sure we add a few here and there doing that, but we should make that up in games where neither Sexson or Broussard played, or played sparingly. So, if we gave every one of those plate appearances to Broussard, assuming his current pace, we come up with the following numbers:

**To find out VORP over 564 PA, I took Broussard’s VORPr (VORP rate per game calculated by Baseball Prospectus), assumed four plate appearances per game (564/4= 141) and multiplied the two together to get 31.4.

For some comparison, 21 HR would put him just behind Paul Konerko in the HR race, a VORP of 31.4 ties him with Kevin Youkilis, 82.9 RC has him relatively equal to Derrek Lee, and an EqA of .279 puts him just off the pace of Kevin Millar and Lance Berkman. (For clarity’s sake, no one in MLB has reached more than 539 PAs for the season so far, but you get the point on the comparison. If you want, compare Broussard’s extrapolated numbers to other first basemen in about two weeks.)

But what I really want to look at is Runs Created and the difference between the two and how that has contributed to wins and losses. We can see from the chart above that Broussard’s RC totals 82.9 over 564 PAs. If we give Sexson 564 PAs, using the same math, his RC totals 58.6 or 24.3 runs less than Broussard over the same number of plate appearances.

If you are like many out there and understand the theory that 10 runs either way adds up to a win or a loss, the Mariners have essentially thrown 1.7 wins out the window by playing Sexson. I come up with 1.7 because, in reality, the Mariners have about 65 RC from the position when you add Broussard’s 23.8 and Sexson’s 41.8. Broussard at all 564 PAs and 83 RC would have given them the 1.7 win extra.

So watch the Mariners down the stretch and the races they are in. If they win or lose by one or two games in the WC or division races, look back to see how much Sexson plays these last two months. Hopefully, McLaren sticks to his plan and keeps Sexson on the pine. It really is their best chance.

Oh yeah, and I almost forgot the most telling numbers that I found in this research:

Richie Sexson 2007 salary: $15,500,500
Ben Broussard 2007 salary: $3,500,000

If you know some of my thoughts on baseball, you know I am an RBI skeptic. I admit it, I am not taken aback by massive RBI numbers like so many of our media and fantasy players these days. RBIs, as I hope to show you, are not a very telling statistic, but they so often contribute to a player getting more All Star votes or MVP votes at the end of the year, or recognition on fantasy teams, etc.

Any good sabermetrician can tell you that RBI are purely circumstantial. They depend on so many different factors: where you bat in the lineup, are you in the NL or AL, who bats in front of you, who bats behind you, how many times you come up with players on, are they in scoring position, how many times do you drive yourself in, how many times do you lead off an inning, etc.

Even in his Sabermetric Manifesto, David Grabiner points out one of the problems with RBI that make them incomplete. And that is simply that “they measure a lot of things which are not the players’ own contribution.” You can’t drive in players who don’t get on base (except by home run. He correctly points out that players who bat behind teammates with high OBPs (”better players” as he puts it) tend to get more RBI.

But it was something he wrote next that made me want to run some numbers to test some things. He states:

In fact, the league leaders in RBI are much more likely to be the players who batted with the most teammates on base or in scoring position (not the batter’s contribution) than those who hit the best with runners on base or in scoring position. Thus RBI are a better measure of who had the most chances to drive in runners than of who was the best at driving in runners.

This is probably quite contradictory to what many people think. It seems logical to think that those who are the best at driving in runs, or who have the highest average with runners on or runners in scoring position (RISP), would accumulate the most RBI. But maybe that’s not the case. Maybe it’s all about opportunity.

So that’s where I want to go from here. I am going to take some of the top RBI men from each of the past three seasons to see which side they fall on. Fortunately, with some helpful tools, we can do this quite simply. Of course, you can find anywhere the league leaders in RBI for the past three years, so that is easy. Our biggest help here will be the RBI Opportunities page on Baseball Prospectus’ website. It lists, amongst other things, plate appearances every player had with men on, the percentage of men driven in, who came up to bat with the most men during a season, and more.

So we will start with the top five RBI leaders in each league from 2004 to 2006:

Geez, I sure didn’t remember that Castilla led the league in RBI just three years ago. Anyway, these are their numbers. So now I will, year by year, break these down by each batter’s average with RISP and then the numbers on base they had during that season and the percentage of those numbers they drove in. I also was able to dig up averages w/ RISP ranks from The Hardball Times for those three years. Now, we can see how each hitter fell into the separate categories. I have included notes on this chart to explain all of the different stats.

What becomes obvious after looking at this chart for a few minutes is that, without even running the numbers, there is a much stronger correlation between high RBI numbers and runners seen on base than there is between high RBI and a high average with RISP.

For the league leaders for each league during these three years, the ranks of number of men on base for the player’s plate appearances are fourth, first, third, second, first, and first. Conversely, the ranks of average with RISP for the RBI leaders in those three years are 42nd, 55th, 5th, 75th, 12th, and 54th. Only one of the six is even in the top ten in avg. with RISP, that being Ortiz in 2005.

Further study of the number shows some pretty telling signs as well. Only three times in the 30 players above was there a batter who was not in the top 17 for ROB during their plate appearances (Dye in 2006, and Rolen and Beltre in 2004). Adrian Beltre, at 27th, has the lowest ranking for any player when determining ROB. For the ranking on average with RISP, there are nine players lower than 27th for their respective year - almost a third of our subjects.

So, apparently, David Grabiner and all other sabermetricians are on the right track. Batting averages can be deceiving and misleading with looked at without any context, even averages as specialized as BARISP. You could have someone who batted .500 with runners in scoring position over 100 plate appearances - but what does that tell you? If that player only had 14 appearances with RISP, you would rather be in the situation with a hitter that bats .315 with RISP but has 44 appearances over 100 PAs.

When batting, circumstances create opportunity, and it is what hitters do with that opportunity that makes them great versus just mediocre. But, like we mentioned earlier, a batter can not control what the players ahead of him do, he can not put runners on base ahead of him who are not there, he can not control how fast they are, and he can not control whether he leads off an inning three times in a game. As with so many things in baseball, how many runners a batter sees on base when he is up has a whole lot to do with luck. A manager knows this, but tries to generally manage that risk by setting his batting order by placing the highest OBP guys in front of the guys who have the most power or the highest SLG% (at least that is how it would work in an ideal world).

Hitters with the most opportunity will have the greatest success rate in driving runs. Whether you are Albert Pujols of 2006 (.397 with RISP) or Andruw Jones of 2005 (.207 with RISP) - you need the hitters ahead of you to do their job before you can start worrying about how good you are at driving them in.

At the end of this baseball season, two of the games best center fielders, Torii Hunter and Andruw Jones, will hit the free agent market. Both are looking to sign long-term deals even though they will both be over 30, and both will be looking for mega-bucks (presumably from their current teams, but perhaps not).

The big difference right now is that one of these guys is having a career year and watching those dollar signs go up and up, while the other one is Andruw Jones.

Partially out of sheer morbid curiosity and partially because I feel like I wasted a third round pick in my most competitive fantasy league on him, I wanted to examine this anomaly that is the 2007 Andruw Jones and see what is really wrong with him and if there are any signs of turning things around. I have a theory here, but I will share it as we get closer to the end. Here’s a clue, though….he really wants to see the money.

I hear people say all the time, “His batting average is so low, and his power is down - that’s why he is having a bad year.” Fine. But WHY is his batting average down and WHY is his power down? He doesn’t appear to be injured. He isn’t very old. It must be something else. Is he unlucky? Is he getting fooled more than he used to? Are pitchers pitching around him more? These are the questions I am looking to answer.

First let’s look at Jones’ production per year since 2000, and then his average year from 2000-2006, and his current 2007 pace for those same numbers:

So the numbers are right here in front of us and they are pretty glaring. Excluding walks, Jones is at least 15% worse than his average season in every one of the these categories. Now we need to start looking at why that is. What is causing a low average, low power, but more walks. For example, you can’t just say, “he has stopped hitting for power,” unless he is injured or old. WHY has he stopped hitting for power?

In order to not to get too confusing here, I am going to list a group of predictive stats where Jones has the lowest numbers of his career this year, and then pick a few to analyze:

Strikeout % - 27.6% (career 21.8%)
Isolated Power - .183 (career .236)
BABIP - .229 (career .284)
RC/27 - 3.77 (career 5.91)
GB/FB Ratio - .82 (career 1.03)
HR/FB - 13% (career 20.5%)
EqA - .245 (career .283)

Looking at these, combined with the fact that, despite his other struggles, he is on pace to draw the third most walks in his career, I think I have noticed a pattern. I truly feel like Jones has fallen in love with the idea that more homeruns will give him more dollars this winter and he is doing everything he can to get every last HR he can. Here are the reasons why I believe this:

Pitch Selection - The relatively high walk rate shows that he has chosen not to swing at bad pitches or ones he can’t handle well. Balls just inside or just outside that he may not be able to drive can’t help him, so he will lay off of them. Balls high or low might lead to groundballs or pop-ups, so he lays off of those too. This leads to more walks.

But, it also leads to the need to swing at almost every pitch in the strike zone, or every pitch that appears to be in the strike zone (sliders, sinkers, etc.). Jones is on pace for 1680 pitches that are strikes this season, the most in his career. Tinkering with your swing, trying to mash homeruns every time up, and favoring the back foot causes severe upper-cut swings (here is the most famous example from this season, a walk-off homerun against the Phillies on April 30) and an increased number of fly balls on balls put in play. Which brings us to the next step.

Ground Ball/Fly Ball ratios - Not mentioned above in the stats is the fact that Jones’ FB ratio is the highest of his career at 45.5%, and that his GB ratio is the lowest of his career at 37.3%. This translates into the number you do see above, which is his GB/FB ratio of 0.82, the lowest in his 10 years in the Bigs. So, almost half of Jones’ balls in play this year are fly balls instead of line drives and ground balls, where the majority of hits come from.

Compound this with the fact that his HR/FB ratio is also the lowest of his career at 13%, and you have a recipe for disaster. More fly balls in play equals more outs being made per time you put the bat on the ball, which brings up the next point; BABIP.

Batting Average on Balls in Play - This stat is becoming more and more popular to try and determine why a hitter has a certain average when it is so far above or below his career norm. A batter with abnormally high or low BABIP can usually attribute it to bad luck, and can expect some regression. But, the same light, high or low BABIP can almost always be directly correlated to success in batting average. We can apply this here to Jones. His BABIP is the lowest of his career at .229. This number is abysmally low, as an average number is somewhere around .290, and Jones is at .284 for his career.

In the second half, one would expect that number to rise just because the law of averages says it almost has to - it really is that bad. But more fly balls do lead to easier outs on balls in play, which leads to less hits and, in turn, a lower batting average. And on the at-bats where he does get a hit, the power is still not following, for reasons we talked about above, and further evidenced in my last point.

Isolated Power - Subtracting AVG from SLG can give an interested party a quick and dirty look at what kind of power a hitter displays beyond just what their batting average looks like. Before this year, Jones’ last time with an ISO number below .208 was his rookie season in 1997 when it was .185, still higher that he .183 he is displaying this year. A combination of many fly balls plus a Line Drive Percentage of 17.3% (below his career average of 18.2%) is leading to much less power for the famed slugger who hit 41 and 51 homeruns the past two seasons. So much so that Jones’ SLG% this year is more than 100 points below what his career average is (.382 to .499).

Now I am not a hitting coach by any means, but my remedy for Jones would be to expand the strike zone a little bit so he can hit to all fields (line drives to all fields, that is), while still keeping the selective eye that allows him to take the walks and get on base. Also, go back to the line drive swing that helped Jones so much in the seasons where he was hitting between 35 and 50 homeruns a year.

Homeruns might look like the key to the offseason treasure right now, but everyone is sure going to be second-guessing a 31-year-old center fielder with a batting average of .200 with no power if he ends the season that way. An all-around hitting approach would do Jones a world of good right now and, more importantly, would also allow him to help save my freaking fantasy team!

I have trouble remembering a year when there was SO much hype about rookies coming up to the majors. Just some common names from this year: Philip Hughes, Hunter Pence, Tim Lincecum, Ryan Braun, Homer Bailey, Elijah Dukes, BJ Upton, Yovani Gallardo, Chris Young, Delmon Young, Alex Gordon, Billy Butler, and Andy LaRoche are just a few names that have dominated club rosters and fantasy teams from the beginning of the year - and some of these weren’t even guaranteed a spot with the big club!

In thinking about rookies, it is always difficult to predict major league success no matter what school you’re from; whether it’s makeup and body type or stats and rates, the guessing game is far from accurate. Keith Law, a former Special Assistant to the GM for the Blue Jays and Baseball Prospectus writer currently working for Scouts Inc. and ESPN, is famous for saying that the player he was the most wrong about was Carlos Pena. Pena had all the tools and all the stats - scouts and managers and GMs drooled over him. Well, ever since he made his major league debut in 2001, he has been traded twice, released twice, made two trips to the minors, and has a career .247 average - and is now 29 and moving past his prime.

This is just one example of how hard it is to predict, even on the sure things. So who knows if great years by Pence, Lincecum, Upton, et al will turn into something more? But I thought it might be fun to look at some of the best rookies from the past and see if very strong rookie seasons translates into very strong major league careers.

My hypothesis is that a even the strongest rookie season does not correlate to major league stardom.

First, we have to determine a sample size. I have chosen to look at more of a modern era for comparative reasons, and therefore look at rookies from 1975 to 2001 (rookies in 2001 have at least 5 full major league seasons and thus a good sample to work with). I want to look at only the best of the best rookie seasons - so I will start with all Rookies of the Year from these 26 seasons. But even that does not help too much because you get your Mark McGwires who batted .283/.370/.618 with 49 HR and you get your Eric Karros’ with .257/.304/.426 - 20 lines - and they were only five years apart. Those two rookie seasons don’t even compare to each other.

So I need a way to establish the best rookie seasons without using stats, because we need a way to look at both hitters and pitchers who were ROY, and compare across a span of 26 years. What I decided to use as my filter is any ROY who finished in the top 25 in MVP voting or top 10 in Cy Young voting during their respective rookie year. This will give us only the best rookie years compared to their peers that season.

For example, Alvin Davis’ ROY campaign in 1984 saw him accumulate a .284 average, 27 homeruns, and an .888 OPS. This would be above average for an offensive player in 2006, but he probably would not have received any MVP votes for that line last year. But in 1984, that line is outstanding. This way, we can compare rookies to their peers and not other rookies that played 20 years later or earlier.

Doing this, we are left with a sample of 17 rookies from 1975-2001:

This list includes 11 who finished in the top 25 for MVP and 6 who finished in the top 10 for Cy Young. Ichiro and Fred Lynn are the only ROY/MVP winners, while Fernando Valenzuela is the only ROY/CY winner.

To look at these players’ careers, we need to think about what stats to use. We could just look at Hall of Fame voting, but half of these players are not eligible, so that does not work. I am going to rely on a heavy dose of rate stats since we are dealing with four players who are still playing today, and we will have to use statistics that range across hitters and pitchers. But I will also look at a few other sorted items that help tell the story such as MVPs, All Star games, etc.

I have included an explanation of all stats in the comments section. All numbers are as of May 28, 2007:

The results I found here surprise me somewhat. All of these players except Pujols and Ichiro have finished their careers or are in their last couple of years. We can save the Ichiro debate for another time, you decide for yourself if you think he should have been labeled a”rookie” in 2001.

The stats here show that the strongest rookie seasons seem to correlate a lot more towards future success for hitters rather than pitchers.

Point in case: Each hitter on this list, assuming no tragic injuries to Pujols and Ichiro, will play at least nine seasons in the big leagues (except for Listach; more on him later). Here are some other examples…

1. Except for Davis and Listach, all other hitters on this list appeared in at least two all-star games that were NOT in their rookie season. So if you take away their great first year, you still see continued success later in their careers
2. All hitters on this list have a career OPS+ of at least 120 (average is 100), except for Listach and Coleman. Coleman is low only because he never slugged over .400, and had 28 career homeruns.
3. Except for Listach, all hitters created at least 200 more runs over their careers than a replacement player would have created (BRAR stat).

“Except for Listach,” is mentioned here three times. You must be thinking, “what about Listach? He proves your theory wrong!”

I agree, he is an outlier in this exercise and skews things a little bit. But one interesting thing about that year’s ROY voting may get us back on track. In Listach’s rookie year of 1992, he beat out Kenny Lofton in a close race. Looking back…..Lofton really should have won. He bested Listach in HR, SB, BB, OBP, SLG, OPS+, Total Bases, and RC. But Milwakuee finished with 92 wins while Lofton’s Indians finished with 76.

Lofton meets all of the qualifications above that Listach does not - 6 All-Star games, 107 OPS+, and 436 BRAR for his career - and he has played 17 seasons so far.

Pitchers on this list are a different story.

The All-Star games by these six pitchers adds up to 15 in 76 total seasons. And 10 of those are from two players (Gooden and Valenzuela).

On this list, you have at least six hitters you can make a legitimate Hall of Fame case for, while there is not a pitcher on the list who comes close to qualifying.

The pitching runs above average range from -7 to 148 - nowhere near the BRAA of 613 for McGwire or 494 for Piazza.

I guess I should not be surprised that the hitters consistently perform better than the pitchers overall on this list. Studies have shown that the peak for hitters and pitchers is from ages 26-28 (use this article as some reference for pitchers’ primes, this one for hitters). Prime years for pitchers tend to last longer than hitters, which is why you routinely see pitchers at age 35 and above still performing at high levels. For the hitters on this list, the mean age is 22.7 during their rookies years, while it is only 21.5 for the pitchers in their rookie years. The hitters compared here came to The Show more seasoned and experienced than their pitching counterparts.

So my hypothesis is only half right. I predicted that an unusually strong rookie season would in no way correlate to becoming a star later in a career. While two of the pitchers on this list (Gooden and Valenzuela) arguably had at least five years of dominant performances, the others essentially amounted to nothing. On the other hand, the hitters on this list average 5.6 all-star games each - and yes, that includes Listach and his zero appearances.

From this small sample, we can make the assumption that a strong showing by a hitter in their rookie year will more likely lead to future success than a strong first season by a pitcher.