In the last 10 years, the percentage of batters faced by left-handed pitchers has dropped off dramatically. James Click looks at the trend and some possible reasons for it.
Baseball teams show a consistent home-field advantage each season, with homer teams playing about .540 ball. Is that edge due to home teams doing a better job of taking the extra base thanks to familiarity with their environment? James Click breaks it down.
The other big piece missing from OBP is the fact that reached on error
(ROE) has also been excluded. If you watch enough baseball, thoughts start
to creep into your head, wondering whether certain players can “generate”
errors to get on base. The poster boy for this line of thinking is
Ichiro Suzuki (or Ichiro! if you live within 100 miles of
Derek Zumsteg). Ichiro!’s speed and batting style certainly appear to make
defenses rush, maybe bobbling a few more balls and leaving him standing on
first after a routine ground ball for anyone else. Others may argue that
there’s a case for players who hit the ball harder than others. Perhaps
they too generate errors, but instead of speed making fielders rush, it’s
the velocity of the ball forcing the error. Thus, since those ROE are the
result of some talent of the batters and not necessarily the fault of the
defense, those plate appearances, rather than being counted against OBP,
should be counted for OBP.
There are several problems with this line of thinking. First and
foremost, there’s still the inherent problem of the official scorer and his
tendencies to rule various identical events as hits or errors, depending on
other factors not relevant to the play at hand. Players who play in front
of “hometown” official scorers will have more of their borderline calls
ruled as hits than players whose scorers who hold the defense to a higher
Second, there may be a difference between infield and outfield
ROE. While there’s certainly an argument that players can generate ROE in
the outfield by hitting a plethora of nearly fieldable line drives, most of
the influence we’re searching for empirically comes from infielders and
their rush to throw out a speedy runner.
Rather than look at the batter’s results in various sacrifice
situations, we’ll look at the resultant base/out situation. The reason
for this is because the sacrifice is a play that both gives the defense a
choice and places it under a great deal of stress. Trying to cut down the
lead runner on a sacrifice is a high-risk, high-reward strategy and
results in a variety of scoring decisions (errors, fielder’s choices, etc.)
that don’t map absolutely to the resultant base/out situation. Further,
the results of a sacrifice can be thought of as falling into three
categories: success, failure, and overachievement. Obviously, when
sacrificing, the batter is attempting to concede himself for the
advancement of the runner. In “success,” the batter is out, but the
runner advances. In “failure,” the runner is out and the batter is safe
at first. In “overachievement,” the runner advances and the batter is
safe. (There is also the possibility of “miserable failure”–a double
play–and a few other rare ending states after errors, etc.) Looking at
the data for 2003 in three baserunner situations, the data yield the
Situation Success Failure Overachievement
Runner on first 61.7 23.5 14.8
Runner on second 60.4 21.2 18.4
Runners on first and second 59.3 25.7 15.0
The data for all regular players from 2000-2003 still shows that sacrifices are almost never a good idea. Putting the 2001 version of Ichiro–the player with the highest breakeven point for Batter One’s AVG–in front of every batter, the minimum expected runs lost by sacrificing over swinging away is 0.018, when Ichiro bats in front of Chris Truby in 2002 and his massive .199/.215/.282 line while he was in Detroit. Using other batters who are also highly adept at taking advantage of a sacrifice for Batter Two yield no situations in which run expectation increases by sacrificing, at least when there’s a runner on first and one out.
Expanding the results to look at other sacrifice situations does not change these conclusions. Looking at the second situation–a runner on first and no outs–and using the same plan of attack, the smallest difference between sacrificing and swinging away is again Truby and Suzuki, but this time the difference is .085 runs. Other players who come close are Craig Paquette in 2002, Alex Gonzalez in 2000, and Pat Meares in 2001 with .100, .107, and .114, respectively. (Not surprisingly, the three players who should never sacrifice as Batter One are Barry Bonds 2003, Barry Bonds 2001, and Barry Bonds 2002, costing the team .466, .481, and .518 runs respectively.)
One of the most striking discoveries of much of the statistical research done in baseball over the last 20 years is that outs are more valuable than bases. This breakthrough means that stolen bases are only good when the stolen base percentage is above a certain break-even point. Furthermore, it means that “sacrifices” are an extremely bad idea if you’re trying to score runs, which we’d like to assume everyone is trying to do–even that team in Los Angeles.
It is one of the most suspenseful moments in a baseball game. There’s a smash to the second baseman, he slides, knocks it down, picks up the ball, throws from his knees, and the first baseman can’t dig it out. The crowds waits, and then the message appears on the scoreboard “On the last play, the official scorer has ruled: HIT.”
Many of the problems inherent in evaluating defense are evident in the situation above. The first, and most crucial, is the fact that one of the most basic statistics involved in defense, the error, is assigned by one of baseball’s loosest rules, left to the interpretation of the various official scorers. While the league has struggled for the past few seasons to remove the subjectivity inherent in calling the strike zone, it has done nothing to remove the same from the assignment of errors. Rules 10.05.a-e discuss in detail what is to be considered a “base hit”–essentially any ball that could not be fielded with “ordinary effort,” a phrase that is never defined or clarified. In any field, statistics are only valuable if they are consistent and accurately reflect the action on the field. Errors, especially recently, have become assigned in such an ad hoc fashion as to relegate the statistic to nearly unusable status.
February, in the baseball world, is the month of predictions. Every analyst, writer, web site, undefeatable computer program, guy with a beer, and book (some better than others) will spend the next month looking over the offseason wasteland and espousing conclusions. The method behind these processes varies more widely than Johnny Depp’s acting roles; some are based purely on numbers, some purely on empirical data, some purely on names, and some purely on nothing. So what can you count on?
For one thing, you can count on me not offering you any spectacular predictions, guaranteed to be more accurate than anything on the market. If you want that, read up on BP’s own PECOTA projection system. Instead, the aim will be to lay a basic groundwork for your expectations of the consistency of basic statistics from season to season. Surmising the volatility of various metrics, and their consistency from year-to-year, is the primary goal.
Alex Rodriguez’s trade to the Yankees has elicited plenty of spirited debate on several related topics, notably what to do with Derek Jeter and his matador defense at short. Reader Mark Shirk had this to say: With an nearly imminent A-Rod to the Yankees trade, I got to thinking about how a move to 3B would affect the value of Derek Jeter. I figured out, using Clay Davenport’s equations, that a move to 3B would mean that Jeter’s RARP would drop about 4 runs over the course of a full season or roughly 154 games. However since Jeter is such a bad defensive player (-22.5 FRAA per 154 games from 2001-2003) the move might actually benefit him. Is it unreasonable to think that Jeter would be 15 runs below average as a 3B? I don’t think it is even out of the realm of potability for him to be only 10 runs below average. All told that is an 8-run gain in value for Jeter, a pretty significant sum. Am I wrong in thinking this?
As promised, here’s a team-by-team breakdown of last week’s NorCal Mock Winter Meetings. With the real winter meetings in New Orleans winding down, it’s interesting to compare the two for like transactions as well as differences.
Tuesday night, Gary Huckabay and I hosted the NorCal version of BP’s Mock Winter Meetings Pizza Feed. The feed was attended by several dozen very enthusiastic fans and one fan’s poor girlfriend, who spent the entire time sitting in the corner wondering how exactly she got mixed up with a group like us. The rules were essentially the same as the Chicago event: Each participant was given a team, constrained by that team’s real-life budget and talent restrictions, and was assigned the task of improving the product as much as possible in a few short hours. Unlike Chicago, we had a few added bonuses. First, our Feed was held after the arbitration deadline, meaning participants already knew whom they had cut and what players they could not sign. Second, we tried as best we could to approximate estimated arbitration awards on an individual basis. While this was much more time consuming, it provided more accuracy when accounting for payrolls and increased the likelihood that teams would simply release players who were likely to command significantly more than a comparable replacement. Third, we made no effort whatsoever to determine deferment of payments–like insurance coverage for injuries like Mo Vaughn’s knee or George Steinbrenner’s brain–or to adjust payroll based on the likely economic windfall that follows signing such marquee free agents as Olmedo Saenz. Besides, often the price of handling the deluge of fan demand for tickets offsets the gains of signing a guy like Olmedo.
Over the last two articles, I’ve looked at various methods for removing some of the complicating factors when looking at team defense. Based on the idea that team defensive metrics were really a measure of three separate factors (park, pitching, and actual defense), we determined one way to remove park factors (PADE: Park Adjusted Defensive Efficiency) and another to remove pitching factors (PIDE: Pitching Independent Defensive Efficiency). By removing these outside influences in our defensive metrics, we’ve leveled the playing field, allowing us to better judge which teams have the best team defense, based simply on the percentage of balls in play that they convert into outs.
With both PADE and PIDE, we removed one factor, but not both. We were able to see either how a pitching staff and defense together looked compared to the league or how a defense and park looked against the league. What we did not have was one metric that simply measured defense versus defense, our ultimate goal.
Last time, we cooked up a way to remove park effects when looking at Bill James’ Defensive Efficiency, a stat that measures the percentage of balls in play fielded by a team’s defense. The new metric, tentatively called PADE, ranked teams on a zero-centered scale, showing how well a team performed against the league average with their given schedule. The intent was to more fairly judge defenses against each other rather than punish teams like Colorado and Boston for having to play in more difficult venues.
As stated before, defense can be broken down into many facets, but the three most prevalent parts are park factors, pitching, and actual defensive performance. Since we’ve already figured out how to remove the first one–park factors–the next logical step is attempting to correct for pitching, leaving us closer to a metric that measures only defensive performance.
To do this, we’ll take a similar approach to the first version of PADE, but instead of defensive park factors, we’ll use defensive pitcher factors. The first step is to determine an expected defensive efficiency for every pitcher, based on their career history.
Evaluating defense has always been one of the more difficult tasks for performance analysts. The first reason for this is that looks can be deceiving. Sure, that acrobatic shortstop playing in the country’s largest market might appear to be a superior defender to the untrained eye, but all too often we draw our conclusions by putting emphasis on the outcome rather than the process of fielding the ball, itself. The second reason is the still-severe limitations we face with regard to collecting data, and how to properly interpret that data once we get a meaningful amount of it. Granted, there are some statistics that can be used when evaluating defense–errors, fielding percentage, Range Factor, Zone Rating, etc.–but none of them is without its flaws.
Which bring us to one of Bill James’ measures for quantifying defensive performance: Defensive Efficiency (provided here by Keith Woolner). Defensive Efficiency is a metric that measures a team’s ability to turn balls-in-play into outs, using the formula (TotalOuts – Strikeouts)/(BIP-HR).
Despite being raw and only applying to entire teams, Defensive Efficiency is a fair measure of overall defensive performance. But that doesn’t mean it can’t be improved.
There have been massive overhauls of the internal structure of baseball
over the last 10 years. Major League Baseball expanded to Colorado and
Florida in 1993, realigned and added the Wild Card in 1995, introduced
interleague play in 1997, and expanded to Arizona and Tampa Bay in 1998.
Each of these changes necessitated a change in major league baseball’s
scheduling, but in each of these changes a balanced schedule was maintained;
the schedule made sure teams played every other team in the league an almost
equal number of times. There was no such thing as strength of schedule.
Starting in 2001, MLB implemented an unbalanced
schedule with the usual amount of fanfare and fan disgust that usually
accompanies such changes. The change increased games between teams in the
same division while decreasing the number of games against other teams in
the league. The reasons behind the change were many, but certainly one of
the most prevalent was that increasing the meetings between divisional
rivals would pique fan interest and peak attendance and, subsequently,
revenue. Who wouldn’t want more games between the Red Sox and Yankees? Or
the Cubs and Cardinals? Or the Devil Rays and Orioles?