Some of you may recall that before being promoted from a FanGraphs Community Research writer to an actual FanGraphs writer, my primary focus was on the relationship between batted ball types (infield fly balls, in particular) and BABIP for pitchers. At the time, I’d been leaving park factors out of the equation in a [vain] attempt to keep things simple, but now I want to give them a bit of attention.
When Bill James speaks, many in the baseball committee listen intently–as they should. James, while certainly not always correct in his theories (and, really, who is), can always be counted on to provide the larger community with excellent food for thought.
In this most recent case, James claimed that ground ball pitchers have essentially been overrated. Per Rob Neyer:
Any analyst can give you a long list of reasons why ground ball pitchers should be the best pitchers. The problem is, they’re not.
Make a list of the best pitchers in baseball. Make a list of the best pitchers in baseball, in any era, and what you will find is that 80% of them are not ground ball pitchers. They’re fly ball pitchers.
…
What I have never understood about ground ball pitchers, and do not understand now, is why they always get hurt. Show me an extreme ground ball pitcher, a guy with a terrific ground ball rate, and I’ll show you a guy who is going to be good for two years and then get hurt. I’m not saying this about Chien-Ming Wang and Brandon Webb; I was saying this before Chien-Ming Wang and Brandon Webb. They’re just the latest examples. Mark Fidrych. Randy Jones. Ross Grimsley. Mike Caldwell. Rick Langford. Lary Sorensen. Clyde Wright. Fritz Peterson. Dave Roberts. They’re great for two years, and then they blow up. Always.
Now, there is a lot that can be teased out here, but I want to focus on the last part of James’ claim–that ground ball pitchers are more injury prone. Are ground ball pitchers (specifically, extreme ground ball pitchers) more injury prone?
When I originally published my findings around CLIFFORD — my metric for predicting players that are at a higher risk of experiencing a collapse in their wOBA (defined as a drop of at least .30 points of wOBA) — I presented a limited number of players for 2013. The list only included six players that qualified under the criteria. As a reminder, players that experienced a significant decline in three out of four metrics (Z-Contact%, FA%, UBR, Spd) were tagged as CLIFFORD candidates. These players had 3.4 times the odds of collapse (53% versus 25% for non-CLIFFORD players).
The single largest driver of collapse was change in Z-Contact% — the percent of pitches in the strike zone that a batter swings and makes contact with. Hitters who saw their Z-Contact% decline by at least 1.4% had 1.68 times the odds of collapsing than those that did not experience such a decline. Since there were far more players that qualified with their Z-Contact% than the full CLIFFORD criteria I thought it would be helpful to share that data with everyone.
Behold!
In my first article, I wrote about the limitations of the linear weights system that wOBA is based on when it comes to the context of unusual team offenses. In my second, I explained how Tom Tango, wOBA’s creator, also came up with a way of addressing some of these limitations by deriving a new set of linear weights for different run environments, thanks to BaseRuns. Today, I will tell you about the next step in the evolution of run estimators — the Markov model. Tom Tango created such a model that can be accessed through his website, and I’ve turned that model into a spreadsheet that I’ll share with you here.
I’ve told you that the problem with the standard run estimator formulas is that they make assumptions about what a hit is going to be worth, run-wise, based on what it was worth to an average team. That means it’s not going to apply very well to an unusual team. What’s so great about the Markov is that it makes no such assumptions — it figures all of that out itself, specific to each team. And when I say it figures it out, I mean it basically calculates out a typical game for that team, given the proportion of singles, walks, home runs, etc. the team gets in its plate appearances. It therefore estimates the run-scoring of typical teams better than just about anything, but it also theoretically should apply much, much better to very unusual or even made-up teams.
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Last week, the Seattle Mariners inked their ace, Felix Hernandez, to a $175 million extension for the next seven years. The dominating righty will be entering his age-27 season this year, meaning the contract will through his age-33 season. That is, unless, he injures his right elbow.
Embedded within Hernandez’s contract is a clause that gives the Mariners a club option for an eighth season — at a paltry $1 million — should Hernandez miss at least 130 consecutive days due to any kind of procedure to his right elbow. The Mariners negotiated this clause after some concern over what their doctors saw in the pitcher’s MRI.
Apparently, the club was reassured enough by their medical staff to sign the mammoth deal, even though the track record for long-term pitcher extensions isn’t the greatest. But how confident should the team be?
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In my last article, I explained how wOBA’s current implementation changes the value of walks, singles, home runs, etc., annually due to changing league characteristics. Does this mean that the value of an event is the same for every team in the league each season? Or in every park in the league? No way. If you’re talking about a weak offense in a high-offense era, then the overall constants for a weak offensive era are probably more applicable to that team. However, it’s not really the point of standard wOBA to guess the run-producing contribution of a particular player to a particular team; I think it’s probably more accurate to say it’s about his probable productiveness in a typical team (although park effects aren’t taken into account, so not exactly… that would be more true of wRC+).
Anyway, Tom Tango realized this limitation, and produced a table that shows how the values change depending on a team’s runs scored. He accomplished this system of “Custom Linear Weights” (“a necessary offshoot” of linear weights, he says) by making use of David Smyth’s BaseRuns formula, which is, in simplest terms, Runs Scored = base runners * (% of base runners that score) + home runs. Home run hitters are not considered base runners, in this equation, by the way. Makes perfect sense, right?
Tango realized that BaseRuns had a better handle on the team run-scoring process than his basic linear weights system (and all the other run estimators), so he translated the results of BaseRuns in various run environments into linear weights. Specifically, the BaseRuns formula told him how many runs the team should score, and the linear weight value of each hit came from how many additional runs BaseRuns expected the team score if it had one more of that type of hit (the marginal value of each hit type). Here are just the basics of his results, in graphical form:
Well, it’s my first assignment as a real writer, having been promoted for my Community Research articles on pitcher BABIPs and ERA estimators, and I’ve been thrown into the deep end of the pool: linear weights. It’s a tricky subject, but I’ll try to walk you through both the problems with linear weights and how they can be overcome. This article series mainly draws from various works of Tom “Tango,” a.k.a. “tangotiger,” the creator of wOBA and FIP, as well as from David Smyth’s BaseRuns. I’ll go deeper and deeper down the rabbit hole of stat geekishness as the series goes on, eventually emerging with a spreadsheet version of Tango’s Markov run modeler that I made for you all to play with. Where the Markov mainly shines over wOBA is when it comes to extreme run environments, such as unusual offenses or extreme ball parks.
Nerds like me, I guess? Tom Tango cared enough to come up with ways to address the shortcomings his original wOBA formulation. If you’ve ever wondered how valuable a certain player is to your favorite team, maybe you should care too; that low-OBP slugger might be more valuable than wOBA might suggest to your low-OBP team. On the other end, a typical walk last year was worth considerably more to the high-OBP Cardinals than it was to the low-OBP Mariners (around 0.04-0.065 more runs each… which adds up over a season).
James Shields was traded this off season from the Rays to the Royals. He has been known for his durability over the years. Spanning the last two seasons, he is first in complete games with 14. Also, he is second to Justin Verlander in innings thrown. The durability and consistency he is known for may be coming to an end. At the end of the last season, he showed signs of breaking down because he was not able to throw strikes and wasn’t able to maintain a consistent release point.
Predicting the ability to steal bases is not something you think you need to do. You did not say to yourself over breakfast, “I wonder if Michael Bourn can steal bases?” You already knew he could. And maybe that’s what made breakfast so delicious.
But if we want to push the frontier of base running, if we want to see the end of the home run era become the beginning of the efficient base running era, we have to do this thing we thought we did not need to do. We have to be able to predict stolen bases.
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I recently wrote about my attempt to design an indicator that would predict when players were at a higher risk for having a collapse-type year. I named the metric CLIFFORD, referring to the fact that players identified by it were at risk of falling off a cliff offensively. My inspiration was Adam Dunn and his disastrous 2011, in which his wOBA declined by .113.
My initial research showed that 58% of collapse candidates identified by Marcel actually experience a wOBA decline of at least .03 (or 30 points)–2.43 times the likelihood of non-collapse candidates. Collapse candidates identified by CLIFFORD actually decreased by at least 30 points of wOBA 53% of the time–2.14 times the likelihood of non-collapse candidates.
Marcel initially appeared to do a better job identifying these candidates. If we knew nothing else outside of just the Marcel projection, our chances were better at identifying collapse candidates than if we used CLIFFORD (and, yes, the difference between the relative risk for both measures is statistically significant).
However, and here’s the bright spot, there was not much overlap between the two metrics.
