Updating BP's metric measuring the monetary value of a player's production.
When the Marlins traded Miguel Cabrera to the Tigers after the 2007 season along with Dontrelle Willis for a handful of prospects, the familiar voices echoed with the following summary: "Baseball is a business." They talked about how the Marlins "could not afford" to keep those players as their salaries escalated, and would only be able to watch them walk away when they became free agents. That’s what they said, at least. Now, the same "they" are outraged that Forbes reported that the Marlins reported the highest profit of any team last season. Clearly, they infer, the Marlins can afford the talent, but choose not to.
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Whether a player's kept or moves on, metrics frequently overstate free agents' future value.
Before a team attempts to sign a free agent, they try to anticipate his value to them by approximating what they feel that player’s production will be over the coming years. Using that, they can then decide what to offer him. There are two prominent methods for evaluating free agents to come out of the sabermetric community in recent years, first here at Baseball Prospectus by Nate Silverin 2005 and later at FanGraphs in 2009. Both have made simple projections for the free-agent crop, and compared those to the salaries they received.
There are reasons why E-BABIP's projections don't always agree with those of PECOTA.
In Part One of this series, I updated my model for projecting BABIP with new 2009 data, and in Part Two, I explained what makes BABIP Superstars and BABIP Trouble-Makers. In this final part, I will discuss some of the hitters where my Expected BABIP (E-BABIP) projections and PECOTA’s BABIP projections differ most, and discuss which number you might want to trust. PECOTA incorporates a lot of information that my model simply does not, but the batted-ball information can be particularly important for certain hitters, and those are the ones where you should place some faith in E-BABIP.
Who ranks among the best and worst in this seemingly unpredictable yet key metric?
In Part One of this series, I updated a model for projecting BABIP, continuing on my previous work from last year. I showed that BABIP can be predicted successfully by looking at batted-ball rates and BABIP on those individual batted-ball types.
BABIP isn't as luck-driven as many suggest, not after you drill down into the numbers.
If you don’t put your bat on the ball, you’re not going to get a hit, and if you don’t hit the ball over the wall, someone might catch it. This series begins with what happens the rest of the time as I develop a model to predict a hitter’s Batting Average on Balls in Play (BABIP). In Part 2, I will explain some of the current BABIP superstars then some of the players where my system differs from PECOTA will be the topic of Part 3.
With SIERA on our stat menu, here's an explanation of why it predicts pitcher performance so well.
It sometimes seems as if the main reason people are wary of Defense Independent Pitching Statistics as a way to measure pitching performance is that they are reluctant to believe the theory that pitchers do not control the hit rate on balls in play (BABIP). It does not make intuitive sense, and it isn't even entirely true. Certainly, fans who disagree loudly with these theories should be reassured by the knowledge that defense-neutral ERA estimators are usually much closer to next year's ERA than the previous year's ERA, but many fans still can't get past the point that ERA estimators usually assume that pitchers do not have control over the outcome of balls in play. That is because these estimators simply look to interpret the effect on scoring of a strikeout, walk, and home run. This gives them the strength to predict ERA well because they are able to explicitly state the effect of each of these outcomes.
What would happen if players had to stay with the teams who originally drafted or signed them as amateurs?
"Loyalty to any one sports team is pretty hard to justify because the players are always changing, the team can move to another city, you're actually rooting for the clothes when you get right down to it. You know what I mean? You are standing and cheering and yelling for your clothes to beat the clothes from another city. Fans will be so in love with a player, but if he goes to another team, they boo him. This is the same human being in a different shirt! They hate him now! Boo! Different shirt!! Boo!"
A look at performance variations in plate appearances over time.
It is well known that hitters do better against a starter the more times they face him in a game. In 2009, pitchers limited hitters to a .256/.322/.405 line the first time through the lineup, while the hitters were able to muster a .269/.333/.433 line the second time they faced the starter, and a .282/.346/.460 line the third time they faced the starter.
Whether they induce ground balls or fly balls, pitchers avoid the sweet spot of the bat at the same rate.
When Eric Seidman and I unveiled SIERA, a little Googling showed that there were three big debates that broke out on the internet. Firstly, sabermetricians debated its validity and value. Secondly, readers debated whether they wanted to see how the sausage was made or the just see the end result, which was how the statistic would be used. Thirdly, baseball fans with a sabermetric bent once again debated the validity of Defense Independent Pitching Statistics (DIPS) Theory.
A surprising revelation that players often do better in the second year of two-year contracts than the first.
Each year, about 25 players receive two-year contracts. The inevitable question that analysts ask is whether it was smart to commit to the player for a second year, or whether the team should have stuck with one year. But did you know that most players receiving two-year deals in recent years actually do better in the second year of their contract? Players who receive three- and four-year deals produce similarly in the first two years of their deals as well, instead of declining as many people believe.
Evaluating single high-profile signings against more scatter-shot solutions to team needs.
In the first twoparts of this series, I explained my new approach to contract valuations and whether MORP should be linear with respect to WARP. Basically, this entailed asking the question of whether Matt Holliday, perhaps a six-win player, could be just as easily replaced by signing two three-win players or three two-win players. The issue is roster space and playing time. The alternative argument to doing MORP linearly is that a team can sign Holliday and concentrate all six of those wins on one spot of the diamond, and then they could improve themselves more by filling their other openings with decent players as well.