Bill James: The Father of Sabermetrics photo found on Google Images

The standards by which the elite are chosen for election into Cooperstown are evolving. This transition can mostly be attributed to the growing popularity and acceptance of Sabermetrics. The decision to vote Ron Santo into the sacred Hall is a prime example of the shift from traditional thinking.

While greatness on the diamond has its conventional and established characteristics, future Hall of Famers may be relying more on Sabermetric measurements to punch their tickets. For position players, the Hall of Fame has been reserved for the great power hitters, those with batting averages over .300, and a high number of career RBI. For pitchers it’s all about the career strikeouts, ERA, as well as wins and losses.

Ron Santo unfortunately passed away in 2010 without ever hearing his name called by the HOF. The Baseball Writers Association of America decision to induct Santo in 2012 isn’t just posthumously honoring a Chicago icon, but is truly a testament to the level of acceptance Sabermetrics has achieved. Santo had a career .277 batting average. He hit only 342 home runs, and drove in a very respectable 1,331 base runners. Santo definitely compiled above average career statistics, but not “elite” numbers to the naked eye. This is where Sabermetrics influenced his worthiness to the voting writers.

Image via Wikipedia

For five seasons, 1963-1967, Santo had an OPS of .905 which would classify him in “Category A” according to Bill James’ “The 96 Families of Hitters.” Category A is reserved for batters with an OPS greater than .900, which is elite and, by all measures, great. He finished first in walks in the National League four times in his 15 year career. He led the NL in OBP three times. Santo was an even better fielder. A five time Gold Glove recipient, he finished in the top five in fielding percentage for third basemen eight times. Santo is currently ranked 105^th all-time in Wins Above Replacement, which is nothing to scoff at.

Ron Santo most likely wouldn’t have reached the Hall of Fame without the help of Sabermetrics. HOF voters are taking into consideration the statistics that are often undervalued and contribute to team wins. Ron Santo helped the Chicago Cubs win and by Sabermetric standards is an elite player deserving of Hall of Fame status.

Sabermetrics will continue to influence Hall of Fame voting. Particularly after an era of steroids and inflated power statistics, Sabermetrics provides a fresh perspective on players who may be on the “bubble” regarding their chance at being inducted. Position players and pitchers who come to mind include Tim Raines, Lee Smith, Jeff Bagwell, Edgar Martinez, Derek Jeter, Andy Pettite, Trevor Hoffman, and Ivan “Pudge” Rodriguez to name a few. Many of those may be considered shoe-ins for the Hall, but these players are nothing short of Sabermetric superstars and shouldn’t (and probably won’t) be overlooked by voters in the coming years.

NPB SP Yu Darvish image taken from Google Images

For many years, American baseball fans with an eye towards the Japanese Major Leagues have been rolling out the red carpet for the Nippon-Ham Fighters‘ 6-foot-5 fireballer Yu Darvish. The NPB (Nippon Professional Baseball) has a strange relationship with MLB regarding their players crossing the Pacific to play stateside.

NPB players can sign with an MLB team by way of free agency, after 9 years of being under club control, compared to MLB’s 6 years. Since NPB players are likely past their primes by the time they reach free agency, the most attractive way for NPB players to face the challenges of MLB competition is via the posting process – NPB teams sell an MLB team exclusive negotiating rights to a player in a silent auction, and if the player and MLB team reach agreement, the player is sold.

American media has made noise about Darvish being posted in offseasons before, but never as loud as it is now. This naturally leads fans to make comparisons, and for illogical reasons (read: racism) Japanese players are most often compared to other Japanese players. This happens among players of other races as well, but since there are far less Major Leaguers of Japanese descent, the sample sizes are tiny and comparisons are highly inaccurate. This is why the comparisons you read of Darvish to the last hyped Japanese pitcher to be posted, Daisuke Matsuzaka, are terrible. Their numbers in the NPB, as well as their skill sets, are vastly different. I will not entertain such a baseline, and neither should you.

I have an interesting comparison for Yu Darvish that most people may not have yet considered (And by interesting, I mean not-Matsuzaka. That adjective you’re looking for there is ass-hatted). Aside from the home run rate, their rate numbers are nearly identical.

Yu Darvish’s last 3 years, compared to Player A in same league:

SO/9: (D)9.7 | (A)9.4

BB/9: (D)1.8 | (A)1.2

HR/9: (D)0.3 | (A)0.6

~

Who might Player A be? It’s Texas Rangers RHP Colby Lewis during his two-year stint with the Hiroshima Carp from 2008 to 2009. Colby Lewis is, admittedly on my part, not a perfect comparison. Darvish has sustained impeccably low ERAs throughout his entire NPB career, which leads one to believe he, unlike others, shows some kind of talent outside of what DIPS numbers can detect. He is likely causing NPB batters to “mis-hit” the ball often or pitches better with men on base. Darvish also throws more innings per year than Lewis did.

Despite these imperfections in my comparison, Lewis is the best base to adjust your expectations of Darvish from. Lewis has given the Rangers 6.9 fWAR over 2 seasons. Considering he pitches in a bandbox and FIP doesn’t adjust for HR/FB rate, I’d comfortable calling Lewis a 4 WAR pitcher, which is to say he’s a solid #2 starter.

Darvish in the MLB isn’t likely significantly better than Colby Lewis. Unless he’s racking up groundballs that I’m unaware of, I think it’s likely his home run rate will stabilize at the level of most MLB flyball pitchers. Will Darvish be able to keep getting batters to mishit the ball in the MLB? My guess is no, considering the kind of non-knuckleball pitcher who does that is a very rare breed, especially among RHPs.

I think Darvish is a 4.5 WAR pitcher in the bigs, assuming he makes all the on-field and off-field adjustments (which is not a safe assumption, by the way). That’s pretty darn good, but not mesmerizing, nor worth the hype and high dollar price tag.

If teams are looking to add another #1/2 starter, CJ Wilson is available for what is likely a lower price and more proven track record.

Most would point to ERA, but that is too easy and ERA isn’t predictable from season to season. The whole idea is to see if you can find individual statistics that may be reproducible and see how they correlate with runs scored. The first categories are simple. We include DER (defense efficiency rating) and FIP (Fielding Independent Pitching) to see whether fielders or pitchers have a stronger effect on runs scored. Those two should have stronger correlations than the others because they are all encompassing statistics. The final two categories break down pitching numbers into the types of balls in play (groundball/fly ball rate) and the other DIPS (strikeout to walk ratio). Since 2011 isn’t even half way through, we will look at 2010.

For those of you that read the last piece, you know these correlations are pretty weak. For what it’s worth, the strikeout to walk ratio is the strongest. One of the things we do notice is that teams that tend to be strong in one area (say DER) are weak in FIP and vice versa. So, put them together and maybe you get something stronger. Still, in order to truly test correlations we need to keep digging. For now, we can use the offensive and defensive scores to rate managers. Like with Pythagoras, it’s a bit weak.

It’s funny how we perceive things. Look at the following data and you would swear that Tony La Russa was overrated. Oops. I think we can agree Joe Maddon is one of the best managers in baseball if not the best. I think we can also agree that Manny Acta is definitely saving his job this year. Still, I would say those rankings are more descriptive than the Pythagorean records.

Voros McCracken published an article on DIPS (Defense Independent Pitching Statistics) a scant ten years ago for Baseball Prospectus. Interestingly enough, the initial theory has been dwarfed in the meantime, but the proposition was simple enough. Pitchers had limited control over balls that are hit in play. McCracken found that strikeouts, walks, and home run rates remained fairly stable over time. Every other statistic had more volatility over time. Naturally, the same would be true for hitters.

In the intervening ten years, a couple of things have happened. First, people have noticed that some pitchers consistently have lower than normal batting averages on balls in play or consistently higher BABIPs. The same is true for hitters as well. So, like good scientists do, other sabermetricians continued to study it.  At the same time, more and more technology and information became available. Sure enough, they discovered that McCracken was mostly correct, but that they could categorize balls in play in different categories.

Those categories include line drives, groundballs, and fly balls. Publications like The Hardball Times have found that batting averages on certain events vary. Naturally, we see a higher average on line drives than the other two batted ball types. Groundballs produce the second highest average. On the other hand, they also noticed that flyballs (while producing a lower average on balls in play) produced the second highest runs per event rate.

Further studies showed that those batted ball statistics were as stable as the so-called DIPS statistics. So, while we cannot predict what will happen when someone hits a line drive, groundball, or flyball, we can predict in general a level of success at the plate based on those numbers. We will look at those numbers from 2010 at all of the positions and on the mound to see how well the 2011 results fit.

From those statistics, other publications develop their own metrics to summarize the ability of a hitter or pitcher. For instance, most of the publications I mentioned last time publish a metric called Fielding Independent Pitching (or something similar in the case of Baseball Prospectus). Those numbers vary because the assumptions of those calculating the metric change. There is nothing wrong with that, but we want to do everything we can to filter out other people’s bias or assumptions no matter how intelligent they may be.

Below we see the 2010 major league averages for different batted ball events (save strikeouts and walks). We take these outcomes and use them when we look at batted ball rates for individual pitchers and hitters. Additionally, we will look at fielders at each position because this will play a big role in the success or failure of pitchers on those staffs. Similarly, teams can use those numbers to better judge who should be a part of those staffs. Naturally, teams can take a look at individual positions defensively and affect change that way. So, without any further ado, here are your league wide batted ball statistics for 2010.

Runs Per Event-ML

LD= 0.38

FB= 0.18

GB= 0.04

Other-ML

K%= 18

BB%= 9

HR/OF= .10

GB= 74% Outs

OF= 83% Outs

Image taken from Google Images

It’s always entertaining when old ideas and new ideas collide. Ask any fan from a bygone era and they will lament the inability of modern hitters to protect the plate with two strikes. They wax poetic about the power hitters of yore and their ability to hit for power and avoid the strikeout. So, preach to them about plate discipline and they will chime in with the chorus. Of course, the modern fan is not so concerned about strikeouts.

Generations in baseball are much longer than generations in sabermetrics. A generation ago in sabermetrics, strikeouts were not considered to be important. We bowed to the God that was walks. The current generation looks at plate discipline as a whole in a similar way that the old-timers looked at it. Those guys didn’t necessarily appreciate walks as much as we do, but the combination is something interesting.

Hear old-time baseball people talk about pitching and you would be led to believe that they get it. Just yesterday, Houston Astros President of Baseball Operations Tal Smith talked for a long time about minor league pitchers learning command of the strike zone. He wasn’t only talking about the ability to throw strikes, but the ability to locate pitches within the strike zone (in and out and up and down). Yet, people like him rarely ever talk about the hitter’s ability to do the same. Look at the numbers and that is proof enough that much of the old-guard really doesn’t pay much attention to it.

The relationship between strikeouts and walks for pitchers has been well established. It would only make sense that the reverse would also be true. However, we can’t always assume such things. Today, we are going to take a look at plate discipline for hitters and how much it relates to scoring runs. We grant that power and speed have a lot to do with scoring runs and they are not represented here. We also are not looking at 2011 numbers purely.

The goal here is unique. We want to find a correlation between the two, but we also want to look at organizational philosophy. The numbers you will see in the table below represent the 2010 numbers for the projected opening day lineups for 2011. Some of those lineups had to change before the season due to injury or other factors. Essentially, we are looking at the best laid plans of mice and men. Plate discipline is measured by dividing walks into strikeouts. For pitchers, a two to one strikeout to walk ratio is the standard. It is the same for hitters, but with regular lineups we should expect something slightly better.

You will notice that in most cases, the plate discipline numbers are worse this year than last year. That really isn’t the case. This year’s numbers include part-time players, pinch hitters, and pitchers. So really, plate discipline numbers are fairly stable over time. The Giants, Cubs, Braves, and Phillies are the only outliers and there are plausible explanations for each. The Pirates and Padres were the only teams to improve and there are plausible explanations there as well.

When working with correlations, I like to start with the general and move to the specific. I’m not a mathematician and I didn’t stay at a Holiday Inn Express last night. I’m making the assumption most of you are in the same boat. So, let’s compare plate discipline ranks with ranks in runs scored:

Exact Ranking= 1

Plus or Minus 1= 7

Plus or Minus 2= 11

Plus or Minus 3= 13

Outlier= 3

Nearly half of the league was within one spot of their rankings in both runs scored and plate discipline. Nearly three-fourths were within two spots. That’s not a great correlation by any means, but we are also only about 25 percent of the way through the season. A big day or bad day by some teams could drop or elevate them two or three spots in the standings. Some representation of power would normally account for nearly half of the explanation of runs scored. Base running efficiency would explain another portion. Then, you get clutch hitting the numbers. However, we cannot deny the correlation here either.

This is where we deviate from statistics and move into one of those questions that made our brains hurt on the SAT (or GRE). If A (2010 regular plate discipline) is related to B (2011 plate discipline) and B is related to C (2011 runs scored) then A must be related C. So, looking at how teams put their “dream” lineup together is valuable in helping us determine how good management teams are in evaluating this data.

Photo taken from Google Images

We finally get to the last of our luck factor series. For those of you that have been following this all along I give a wholehearted thanks. If you are just joining us you might want to back track and read the first one. It should be archived in the sabermetrics section of this site. There is nothing quite as volatile as charting bullpen performance. However, there are some common elements we notice across the board.

Obviously, defense efficiency ratings should be fairly stable overall. We notice a slight improvement for relief pitchers. There are any number of explanations we could latch onto. Relievers tend to be harder throwers, so more of them are fly ball pitchers. Fly balls tend to become a higher percentage of outs than groundballs. It is more likely though that the change is due to fielders behind the pitchers. Most teams employ defensive replacements in close games. A slow and plodding outfielder is replaced with a fleeter one. This almost universally happens after a starting pitcher has been removed.

Bullpens have higher strikeout rates and higher walk rates. These in combination lend themselves to volatility. On the one hand, bullpens will naturally have higher left on base percentages because of the pitcher’s ability to strikeout more hitters. Then again, the higher walks will lead to more base runners.

There is more volatility among relief pitchers because of the smaller sample size, but we still see some trends. San Diego was the only good pen to suffer from bad luck. Had they had even average luck the Giants wouldn’t have even made the playoffs. That is something to look forward to when the season starts. Meanwhile, the Giants had the best luck out of all of the NL teams.

These numbers are from the 2011 projected bullpens. A quick look over the strikeout and walk ratios will provide one clue in addition to the luck factors. Of course, the FIPs are also a good predictor for future performance. For team predictions, this has to give us pause before picking the Phillies to run away with it all. If we take FIP into consideration then the Phillies won’t even finish in the top half in the NL in ERA for relievers. It’s hard to pick a team to win 110 games or more with a below average pen. The Giants on the other hand are in excellent position to repeat with their rotation and bullpen at the top of the league.

Photo taken from Google Images

Hopefully, you are following along with the luck factor series. This will be number five in that series. If you haven’t checked it out yet, I will try to give you a quick hook before we dive into the numbers. Fantasy baseball players have many of the same concerns as general managers. When you look at the free agent market you want to differentiate between the guys that had career seasons and those that are really good. Then, you can find the occasional diamond in the rough that gets you over the top.

Albert Pujols and Alex Rodriguez will obviously help every fantasy team, but the winners are the ones that find the pot of gold at the end of their fantasy draft. Heck, many of the best players were found on the waiver wire after the draft. The guys that had Ben Zobrist in 2009 probably did pretty well. Carlos Quentin had the same affect a couple of years ago. Last year, guys like David Price and Brett Gardner helped teams excel. Everyone wants to know how to find those guys and avoid guys like Jason Bay that went in the other direction.

For us, we are looking at teams as a whole. Which teams will be the surprise team like San Diego was last season? Which team will disappoint us as the Angels did? If you look carefully enough you can find this year’s Texas Rangers. While that might not help fantasy owners directly, we do see that good teams usually have a lot of players that overachieve. Luck factor can help us with these critical questions. Hopefully, I’ve gotten your attention, so I’ll review the process briefly.

Luck factor is essentially the difference between a team’s performance and the league average in numbers where they do not have direct control. For pitchers, those numbers include defense efficiency rating (DER), home runs per balls hit in the outfield (HR/OF), and percent of runners left on base (LOB). The presumption is that since pitchers do not control these numbers completely then luck plays a part. Luck factor is the representation of that luck. It simply is the difference between those numbers and the league average. The obvious implication is that we can expect some regression on both ends. In other words, those numbers should move towards the middle.

Some of you that are reading this are more versed in sabermetrics than others. For those, I should point out that luck factor is purely a qualitative metric. It is only meant as a general predictor and not a specific one. The bigger the number on either end the stronger the luck, but other than that the numbers have no specific meaning. Teams and sabermetricians alike never want to settle on luck. Luck is what allows you to win in Vegas. It isn’t a good model to base your franchise on. So, we’re constantly looking for strong correlations that can be replicated. In other words, can we find something other than luck to hang our hat on?

In the American League tables we noticed that LOB numbers were strongly correlated to DER numbers. That makes perfect sense. Ironically, pitching numbers are probably easier to link to other batted ball metrics. The Oakland Athletics signed a bevy of pitchers with high flyball rates. They have an outfield with above average fielders and a very spacious home ballpark. The combination gives those pitchers a higher chance of success. The same is true in parks like Petco Park and Citi Field. The inverse would be true in Coors Field or Ameriquest Field. Teams that pay attention to those factors (along with where their stronger fielders are) end up succeeding.

It shouldn’t surprise anyone to see most of the positive luck factors on top and most of the negative one on the bottom. Yet, we see a few individual anomalies inside. The Phillies and Padres virtually were even in terms of luck which means we should be able to predict similar numbers for them this year. The same is true of the Reds and Rockies. Meanwhile, teams like the Diamondbacks and Mets should take a tumble.

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Photo taken from Google Images

For those who have no idea what I am talking about, the last time I looked at projected lineups for the American and National Leagues. Essentially, luck factor is a crude metric I created to account for the differences between a hitter’s BABIP and the league average. Other factors are included as well. Grounded into double plays and home run per outfield rates were also included. For pitchers, the luck factor is a mirror image. In their case, we look at defensive efficiency rating, home run rate (like with the hitters), and left on base percentage. I usually like to take a quick look at the numbers first, but here’s a few quick words before we begin. The numbers you see represent 2010 numbers but projected 2011 rotations. Each team has six starters in their rotation. Obviously very few teams make it through with only one extra starter, but we have to cut it off somewhere.

With the hitters we did not see any strong correlations outside of luck factor, but here we do see one major correlation. As DERs go up so do LOB percentages. This makes perfect sense of course. If you can get to more balls then fewer hits will occur in all situations. So, if you improve your fielding then you go a long way to improving ERA. Naturally, this means that FIPs are far more predictive than ERA.

Considering that the Blue Jays have shed so much payroll over the last few years (dealing Roy Halladay, Vernon Wells, and Alex Rios) they are likely to fly under the radar. With even an average fielding season, the rotation will surprise a lot of folks. Count in a fairly dynamic offense and they could challenge for as high as third place in the daunting AL East. On the flip side, the Mariners and Athletics look better than they actually are. Obviously, in the Mariners case this is a bit scary indeed.

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Photo by Icon SMI

If you think of the great power hitters of all time—Babe Ruth, Lou Gehrig, Willie Mays, Hank Aaron—the names that come to mind were more than power hitters, since they were all around fantastic batters. The traditional definition of a slugger did not necessarily include hitting home runs, but rather, hitting the ball solidly and often. In the right circumstances, a hard grounder up the middle was just as exciting as a 450 foot bomb.

Back in the day, in order to be viewed as a great ballplayer, a slugger needed to be able to perform well in all other aspects of the game. The Bambino’s career batting average is .342, and Gehrig’s is .340, both higher than contact hitters such as Tony Gwynn and Rod Carew. Hank Aaron’s career runs scored is tied for 4th all time, proving that he was an excellent base-runner. These are just a few examples.

However, nowadays, most home run hitters focus only on hitting home runs, disregarding all other components of the game. This phenomenon can be attributed to many causes—the Designated Hitter, the failure of fans to appreciate all facets of the game, and the popularity of the home run. In any rate, the emergence of these new power hitters is hurting their teams and the game itself.

The first modern power hitter seems to be Bobby Bonds. Though Bonds did swipe bases effectively throughout his career, his strikeout totals were unheard of for his time. His career totals in batting average and on base percentage are pedestrian, and his slugging isn’t overwhelming either. The number that jumps off the page is Bonds’s strikeout total, 1757 K’s in only 14 seasons. Per 162 games, he averaged 154 K’s. Because he was respected as a three-time all-star and a solid ballplayer despite his strikeout numbers, Bonds lead the way for the new style of power hitters.

Another early example is Reggie Jackson, the strikeout king. His postseason heroics cemented him as a legend, and the guy knew how to hit home runs. But, that’s just about all he knew how to do. That, and strike out. Mr. October never drove in that many runs, or stole that many bases, or drew that many walks. His only proficiencies were hitting the long ball and “K-ing.” Yet, because of those heroics, and the fact that he played the prime of his career in the Big Apple, Jackson is considered one of the finest hitters of his day, a claim that simply cannot be supported.

I believe that these two players changed the game forever. A lot of my friends criticize baseball because many of its stars seem to be out of shape. Most baseball players in the game today are in good shape, but the ones whose fitness is suspect stand out: David Ortiz, Prince Fielder, Paul Konerko, Ryan Howard, Adam Dunn, Jim Thome, and more. Some of these guys are “large,” and some of them just can’t run. Regardless, to play in the Major Leagues, a player should be able to be successful in more than one facet of the game in order to help their team.

Most power hitters of today strike out in bunches, even infielders like Alex Rodriguez. Take a look at the stats of Adam Dunn or Ryan Howard, and you’ll notice a pattern: low to average batting averages, lots of strikeouts, no stolen bases, and lots of home runs. This is a common model of the average power hitter of the 2000’s.

RYAN HOWARD

ADAM DUNN

ALBERT PUJOLS

VLADIMIR GUERRERO

One of Bill James’s most interesting stats is the “similarity score.” According to baseball-reference.com, “To compare one player to another, start at 1000 points and then you subtract points based on the statistical differences of each player.” Dunn and Howard over the years have been scored similar to players like Richie Sexson, Norm Cash, and Daryl Strawberry, none of which are Hall-of-Fame quality ballplayers.

While most power hitters nowadays are “one-dimensional,” there are a few exceptions: Albert Pujols and Vladimir Guerrero are versatile ballplayers with good power as well as plate discipline. Using similarity scores, the two players have been compared to Joe DiMaggio, Jimmie Foxx, Willie Mays, and Duke Snider, all of whom are legends of their time. Despite this, most power hitters of the day can’t run, can’t hit for average and strike out far too frequently.

In many home run hitters’ contracts, there is a clause stating that they get rewarded for hitting a certain amount of home runs. A possible solution to the “one-dimensional” problem is rewarding a player not only for hitting homers, but striking out less, or stealing bases, or hitting for a high batting average. The home run is an important part of the game, but it shouldn’t be rewarded more than any other part.

Overall, power hitters today specialize in just hitting homers, which has changed the game for the worse. Many of them aren’t athletic enough to even play a full season, and baseball gets a lot of negative commentary because of it. I don’t know if the game can ever be the same again, but I hope it will change back to its true self eventually.