DISCLAIMER: I know certain players are more likely to outperform/underperform the league-average BABIP based on their specific player profiles. This is just a fun exercise to consider if everyone’s “luck” was the same.

With that disclaimer out of the way, I began wondering who the best/worst hitters are in baseball if batted-ball luck didn’t figure into the equation. We hear analysis frequently about Player X who’s having a breakout year, and the refrain is consistently that he is having better luck on batted balls than he had been having in the past. For example, BABIP was one of the main reasons cited for how Chris Johnson batted .321 in 2013 after hitting .268 over the previous two seasons. Many people look for fantasy sleepers based on a much lower than normal BABIP. The effects of BABIP are undeniably real and have been well documented. If we take BABIP out of the equation though, who rises to the top?

Before we get to the results, let me go over my methodology. It’s extremely simple, and you can probably guess how this is done. If you don’t want the boring details, please skip ahead. The first step to these calculations was to keep all factors not included in the BABIP formula constant. Each player still hits the same number of home runs. Each player still walks at the same rate. Each player still strikes out the same amount. The only component that changes is hits that don’t leave the yard (1B, 2B, 3B). I took the denominator of the BABIP equation for each player (AB-K-HR+SF) and multiplied it by the league-average BABIP (.297). This gives us the number of non-HR hits a player would have tallied if luck was removed. To get the number of singles, doubles, and triples each player hit, I took the ratio of Actual Hit Type/Actual Total non-HR and multiplied by expected non-HR. For example, Mike Trout hit 115 singles, 39 doubles, and 9 triples in 2013. That means that 70.6% of his non-HRs were singles, 23.9% were doubles, and 5.5% were triples. When adjusted for BABIP, we would expect Trout to hit roughly 129 non-HRs this past season. Multiplying 129 by the component percentages gives us roughly 91 singles, 31 doubles, and 7 triples. Everything else remains the same.

Batting Average Leaders

To answer the question posed in the introduction, we can look at many different stats. We already discussed how much an effect BABIP can have on a batting average, so maybe we should start there. For what it’s worth, the MLB leader in BABIP in 2013 was Chris Johnson at .394, and the worst BABIP belonged to Darwin Barney at .222.

Looking at those tables, the first thing that jumps out to me is that only two players (Edwin Encarnacion and Miguel Cabrera) in all of Major League Baseball would have hit .300 last year if luck is removed. The American League seems to possess better luck-independent hitters as the NL “batting champ” would have finished tied for fifth in the AL. Also, if Andrelton Simmons could actually hit .291 each season, he’d be an MVP candidate. I also find it interesting to look at which players benefited and suffered the most from their respective BABIPs.

On-Base Percentage Leaders

Perhaps we shouldn’t limit ourselves to just simply batting average. Isn’t it more important to avoid outs that it is to just get hits? Let’s look at the OBP results.

Once again, only two hitters (Encarnacion and Cabrera) would have reached based at a .400 clip. A trend is definitely starting to emerge. The gap between the AL and the NL is much less pronounced here though. As for the biggest changes in the MLB, consider the following tables.

As you might expect, these tables don’t look all that much different from the batting average change tables. Other than some reordering, the only difference here sees Allen Craig replace Mike Trout on the most negative change table.

On-Base + Slugging Leaders

Getting on base a lot is a promising start, but you win baseball games by scoring runs. What hitters were best at driving the ball while avoiding outs? Let’s look at the OPS results.

Once again, our Top 2 are Encarnacion and Cabrera, with a considerably gap between Cabrera and third place Chris Davis. The AL/NL split is at its most pronounced here. To see if our trend in the biggest changes tables continues, consider the following tables.

The trend continues as expected. Also, the negative regressers are harder hit than the positive regression candidates.

Weighted Runs Created Plus Leaders

This is FanGraphs though, so we can’t simply look at traditional stats. We need something that’s park-adjusted and comparative to league average. Let’s look at wRC+. (NOTE: These numbers aren’t adjusted for individual leagues as is normally done with wRC+. I’m lazy and didn’t take the time to do that extra step, so the wRC+ values won’t make up exactly with what is listed elsewhere on this site.)

As you might expect, Encarnacion and Cabrera top the charts again. Paul Goldschmidt is once again the National League leader. As for the biggest movers, they look very similar as well as you might expect.

Since we looked at the leaders in each category, let’s look at those who failed to meet such lofty standards in 2013.

Batting Average Laggards

The most startling thing I notice from these tables is that Dan Uggla gained .042 points in his batting average and still finished last in the league. Now, that’s impressive.

On-Base Percentage Laggards

Michael Bourn is our only carryover from the batting average tables that appears on the OBP tables as well. Probably not a great sign for Cleveland.

On-Base + Slugging Laggards

Uh-oh, Bourn is back again, and the only player relatively close to him is Adeiny Hechavarria. Hechavarria is a fine defensive shortstop who has noted offensive woes. Bourn was a big free agent signing for Cleveland expected to jump start the Indians offense. Those represent completely different expectations.

Weighted Runs Created Plus Laggards

Nothing here is meant to be used to draw conclusions about any hitters. I’m not advocating for Edwin Encarnacion as the best regular in baseball or Starlin Castro as the worst. I just thought this would be an interesting simple exercise to consider. Just for fun though, let’s look at the AL MVP race one more (“luck-independent”) time.

If we take luck out of the debate, Cabrera is an 8% better hitter compared to league average than is Trout. I guess the BBWAA doesn’t think Trout is an 8% better fielder and base runner than Cabrera. Surely they know what they’re talking about though. I mean they do get to decide who belongs in the Hall of Fame after all. They’re the smartest baseball folks out there.

The debate on relievers in fantasy baseball continues to rage in our Ottoneu league – and today, RotoGraphs’ Brad Johnson joined the fray with an article on the subject inspired by our intrepid commissioner @Fazeorange.  To be fair to Brad, he stated up front that his goal was not to resolve the argument, but rather to present a framework to answering the question.  Given my trademark immodesty, I’d like to offer a high-level (and what I consider rather obvious) answer to the question.

Beyond my endless rants on the subject, this question arises from a real-world situation: one of our best and most active owners (and a former champion) has, at least on the message boards, been championing a strategy whereby he stacks his bullpen with “elite” relievers, thereby projecting an enormous bullpen advantage that not only confers extra points, but added flexibility elsewhere (because elite RPs are cheaper than elite position players or starting pitchers).  His bullpen currently includes 3 of the top 5 RPs from last year, and were LWTS scoring retrospective (like a Strat-o-Matic league, perhaps), this makes sense.  However, the question is whether it is likely his bullpen is filled with elite 2014 RPs.  Unfortunately, that is unlikely the case.

To attempt to gather data that might answer the question (if you want the entire spreadsheet, please email me), I went back and gathered the list of Top 10 projected players at each position (25 for the OF) in March 2012 and March 2013 (i.e., immediately prior to the beginning of the season).   I used ESPN rankings, though I’m not sure that the lists matter, the variation at the top is minimal each year.  I then pulled each players Ottoneu LWTS point total for that season, and compared that number to the replacement level for his position in that year.  Brad’s replacement level for RPs was 72 (on the assumption that each team rostered about 5 RPs, and perhaps a bench player – in other words, sorting by RP scoring the 72nd-ranked player).  For my positions, I defined the “replacement” player as follows:

[** Interestingly, the replacement 3B in each year, finishing 18th each year, was Alberto Callaspo – at the gut level, this gives me some confidence we are defining replacement level appropriately]

Next, I summed the PAR (or, in some cases, the points below replacement) for each position.  Broken down by position and year, the results are as follows:

What do we see?  Surprisingly, the level of production for the top 10 projected players across the positions versus our replacement level is remarkably constant – except for RP.  If you invest in Robinson Cano, or Adrian Beltre, or Clayton Kershaw, you can be expecting 300-350 Points Above Replacement.  The only outlier here is RP – if you invest in an elite RP, you can expect to receive and extra 50-60 points from that investment.  Why?  Well, because even amongst the top 10, the flame-out rate is significant.  In 2012, here is the projected top 10, along with their actual production and PAR:

Craig Kimbrel was the #1 ranked RP and performed like it.  Mariano Rivera blew out his knee shagging flies, while Brian Wilson wrecked his elbow as pitchers do, and both delivered far below replacement-level value.  Rafael Betancourt and Joel Hanrahan managed to deliver replacement-level to slightly above performance – but presumably at elite prices.

Was 2012 an outlier?  Here is the same table from 2013:

Is there a pattern?  Sort of.  Craig Kimbrel?  Monster – go get him if you can.  3 of the 10 posted wildly below replacement level.  A couple other big names Papelbon and Rodney) managed slightly above replacement, and a couple (Chapman and Nathan) were excellent.

Of course, this is a first-level review – as always in Ottoneu, price matters, so a $2 Brian Wilson headed to Tommy John doesn’t affect things very much.  However, the question we’re trying to answer here is whether or not investing in the best RPs pre-season makes sense.  Regardless of price, in my view it doesn’t because there is such little likelihood that we can identify them if they’re not wearing a Braves jersey and closing in Atlanta.  Why does this debate rage, at least in our league?  My suspicion is that, as we look back year over year, it can be difficult to remember which relievers were expected to be elite – those sitting on Koji Uehara now can scarcely remember a time when he wasn’t atop the RP leaderboards.  Nevertheless, if we look at the numbers, RP is the one position that investing in anyone not named Craig Kimbrel makes little sense from the perspective of Points Above Replacement.

Thoughts?  Issues?  Problems with my methodology?  General screeds?  All are welcome, either though the site or via email at bill dot porter at gmail dot com.

(Also posted on my blog:  sportsbythenumbers.wordpress.com

When I saw that Clayton Kershaw signed a seven-year contract with an average salary of over $30 million per season, my first thought was: that’s definitely going to be an albatross contract. In my mind, anything over $20 million per season has always seemed to be that threshold where a player no longer has a realistic chance of performing to the value of the contract. But, I am smart enough to know that what is in my mind is not always the same as what is in reality, so this brief post will look at every player during the 2013 MLB season that collected a salary of $20 million or more. My goal was to get a rough idea of exactly how many players either outperformed, adequately performed, or underperformed their salary.

I collected data from the website baseballplayersalaries.com. In the table below, I’ve reported the names, teams, and estimated salaries of each $20 million plus player in the 2013 MLB season. I’ve also reported the percent of their team’s payroll each player received and the percent contribution that player made to the team’s on-field performance.

Table 1: Players with $20 million or greater salaries for the 2013 MLB Season

The first thing I noticed was the number of players that underperformed their salary — 13 of 18. That’s just over 72%!

When it comes to players that I consider underperformed, there are too many to list. So, instead I’ll list the players who I consider adequately performed to their salary for the team they were on: Joe Mauer, Miguel Cabrera, Adrian Gonzalez, and Justin Verlander. That’s only 4 of 18, or about 22%. I did not include Cliff Lee in the list because he clearly outperformed his salary based on this measure. He was the only one of 18 players to do so. That translates to only 6% of players with $20 million plus salaries outperforming.

Even more staggering was when I calculated the average percent of team payroll an individual player on my list made, and compared it to the average percent of team on-field performance. The average player on the list made 14% of their team’s payroll, but only contributed to 5% of their team’s performance.

I realize that the criteria I have used is limited in many ways. For example, players on a poor-performing team (ex. Cliff Lee) will have a higher percent of on-field team performance, and vice versa. Or players on a team with a low total payroll will have a higher percentage of team payroll. However, I feel these numbers are so overwhelmingly lopsided that I’m not sure if you would be able to find any objective criteria that would show an opposite trend.

Given that these high-paid players consistently underperform their salary, an entire new set of questions arise. Why are teams still so willing to hand out these contracts? Do underperforming ‘star’ players really generate enough additional team revenue to justify their cost? What would happen if a large-market team properly valued their players?

With the precedent set by the Kershaw contract, maybe in the not-so-distant future $30 million will be the new $20 million, but as of the 2013 season a $20-million salary almost guarantees the player will not be getting the short end of the stick on that deal (in terms of performance at least).

This post originally appeared on my blog Biotech, Baseball, Big Data, Business, Biology…

The world would be a simpler place, although maybe a much more boring and predictable one, if every aspect of performance could be measured directly. My completely unoriginal thought here is that one of the reasons sports appeal to so many people is because they provide clarity. In a confusing, complex world where the NSA is sucking up our information like a Dyson vacuum sucks feathers in a henhouse, and we’re told this is for our own good, clarity can be refreshing.

The simple view of an athlete’s performance is that all the accolades (or jeers), all the milestones (or flops), all the accumulated statistical totals (or lack thereof) are because of that athlete’s ability: his or her drive, passion, training, and natural ability. And that performance is measured via the statistics each sport collects and chooses to honor and promote. Performance is right there, what more do you need? What more could you want?

But much as we might find the simple view intuitive and appealing, it’s also incorrect. Not only are some of those statistics, at best, clearly crude proxies for true ability, they are also often (always?) dependent upon context. Where and to whom did that quarterback throw all those touchdown passes? Which coach directed that basketball player during the prime of her career to play in a style that complimented (or confounded) her natural tendencies and strengths? What elements of that slugger’s personal life were in shambles the year he broke into the major leagues and thrived/struggled, and what difference did it make?*

If sports analysis is moving in any direction, I like to think it’s moving towards a nuanced, humble view of sports performance that accepts the statistics, the measured performance, the team won-loss record, as proxies at best, distant cousins twice-removed from what we are most curious about: who’s good? How good? Was/were he/she/they ever the best? What does this record mean in absolute terms, if such a Platonic thing could ever exist? We might try to find better ways to measure performance and context, but we’ll always be approaching the asymptote, never quite getting there.

And if athletic performance is so hard to measure, how much harder is it to measure those whose actions are another step yet away from the statistics, the solid measurements produced on the field?

What makes a good manager or coach, and how can we tell?

This is a topic of endless debate, and for good reason. Although they are not often paid like it, there is among many a feeling that the manager or head coach is one of the key elements underlying athletic and team success. As Bum Phillips said of Bear Bryant, “He could take his’n and beat your’n, or take your’n and beat his’n.” This is maybe the ultimate expression of the belief that a coach is what makes the team what it is.

Whether that’s true or not is the question, though. It’s clearly not simple. There have been efforts in the sports analysis community to try and figure out how much coaches and managers matter although sometimes these efforts suffer a little too much from retrospective analysis. For example, “these managers’ teams had winning won-loss records, so therefore they are better managers. Let’s look at the traits they have in common and say those are the traits that let us classify managers into good and bad.” These kinds of analyses are, I believe, over-fitting the data, and it’s often not long before contrary examples pop up.

So what to do? Well, a working paper put out by the National Bureau of Economic Research showed one possible way (thanks to @freakonomics and @marketplaceAPM)

By how much can a good boss improve productivity? About 10%. @freakonomics explores the influence of bosses: http://t.co/LXVDWAqZ

— Marketplace (@Marketplace) December 26, 2012

The methodology may not be completely applicable to the sporting environment, or even to most business environments but sports I think is a closer match than most because of the nature of player and coach movements (as we’ll see in a bit).

This study, by researchers at Stanford and the University of Utah, attempted to answer the question of how much bosses are worth to employee performance. And the method they used, frankly, was based on brute force. They first had to find a business situation that would offer them a huge sample size (23,878 workers, 1940 bosses, and 5,729,508 worker-day measurements of productivity) and a clearly quantifiable and electronically captured measure of productivity: technology-based services (TBS). Think of jobs like retail clerks or call center operators where specific actions are repeated and logged; the specific business that was studied remains nameless as a condition of the research. And the third characteristic that made this work is that this particular company also moves employees from boss to boss on a regular basis — in general once or twice a year.

Let me digress for just a second to expand on why this is so important. In clinical research the gold standard is the double-blinded, placebo controlled trial. Which this was not. But it’s a good deal more rigorous than an anecdotal, under-powered observational study. Essentially, their study design is a retrospective, (effectively) randomized crossover study. This allows the performance of each individual to be compared both within the period of time he or she is working for a given boss as well as across different bosses. The accumulation of so many data points allowed the researchers to build statistical models that could isolate the effect of specific bosses on performance even given the vast amounts of noise that are inherent in the day-to-day performance of these employees.

In addition, their model is designed to discovery, a priori, which bosses are best rather than relying on any information from the company under study. In other words, factors such as won-loss records and championships and media savvy don’t enter into the equation. Whether the company or the researchers are going back to their data and corroborating it now with surveys and opinions of the employee, bosses and upper management I don’t know, but that would be fascinating, wouldn’t it?

To give a very high level of summary of their work, they created a mixed model of human capital as the product of talent and effort. Each of those two elements was then further broken down into components that are and are not under the influence of one’s boss. Next, estimation methods were used to approximate the relative effect of different components within the model, including those due to the boss, based on the shape of the overall dataset.**

They uncovered several possible effects in their analysis, the primary one being that top bosses can result in about a 10% increase in the productivity of his or her group relative to the worst bosses. They also found that a good boss seemed to affect worker retention, and that there was a small but significant effect of pairing good workers with good bosses.

Generalizing the specific findings directly in any way to sports management is completely unwarranted. There are several key differences between the situation they analyzed and the team environment; these should not be overlooked, such as: the diversity of actions taken by any individual athlete in a team setting (as opposed to rote, repetitive work like taking reservations in a call center); the effect of peers in a sports environment likely being greater than the work situation described in this study (i.e., workers being generally autonomous in their tasks), and that athletes often get different bosses by moving between establishments (teams) whereas the current study examined a single company.

However, what I think is worth exploring is the question of whether a similar methodology could be applied to sports teams. Let me just say that I will not attempt an exploration myself, I’m just pointing out the possibilities. So anyone hoping for a big take-home message can stop now. Sorry for taking five minutes of your life!

Here are what I see as the requirements of a sport that would allow generation of a large and diverse enough dataset.

1) Specific measurements of output. As discussed above in the way-too-long-winded introduction, one thing sports has plenty of are measurements of output. Except soccer. What do people measure in soccer? YouTube video highlights of great runs followed by missed kicks?***

2) A large number of transitions of coaches/managers and players between situations. Fortunately in this age of free agency, trades and hot seats, there are routinely numerous changes of players and coaches/managers every season. Also fortunately, players and coaches/managers often get multiple chances with different teams and situations.

3) Enough data. This is a tough one. Off the top of my head it seems baseball and basketball are really the only sports that have enough granularity, a long enough season, and sufficient numbers of teams and players to make this work. Maybe hockey. American football, probably not.

However, it seems worth a try. To explore this idea further with baseball as an example, one could choose to isolate one component of performance such as a hitting statistic. Since we would want to measure something that is both generally agreed upon as positive and also something that stabilizes relatively quickly to best reflect effect of coaching, one could pick strikeout rate (60 plate appearances (PA)), walk rate (120 PA), or singles rate (290 PA). It should be stated up front that this means the effect of the manager only on that particular skill will be seen. Probably the entire analysis would need to be repeated for each of several offensive statistics to create a composite and granular picture of how a given manager influences players under his direction.

One could then use individual game performance as the time component of the model and collect data on that specific metric over time and relate that to which managers a given player had and for how long. The null hypothesis would be that the effect of managers would be nothing, and so the result of the model we would look for are signs that specific managers do make a substantial difference in performance by the players under his instruction compared to those same players before and after being on that manager’s team.

Is there enough data for a signal to be seen? You know, in my day job as a genomics researcher this is probably the main question I get from scientists wanting to perform an experiment: is the number of experimental subjects big enough? And my answer is always the unsatisfying, “We won’t know for sure until we do the experiment and compare the natural variation to the effect size.” Same answer here.

And why bother? Well, I go back to what I said earlier about approaching the asymptote and trying to learn more. Not just in sports, but in so many other parts of life, there are elements that right now are in the realm of intuition and anecdote and subjectivity. Who’s a good CEO? What public policy interventions do good and, more important perhaps, are the most cost effective? Wired magazine just had a nice article about the use of controlled trials to measure the actual effect of public policy interventions in the developing world. In our search to make the world a better and more understandable place, we owe it to ourselves to keep asking questions and trying to come up with ways to answer them.

*Notice here, by the way, that you can take either situation–thriving or failing–and make up a completely believable story in your head about how that player’s personal life played a role in his performance. How he rose above the conflict, or the field was his refuge, or his anger or frustration fed his on-field performance. Alternatively, how he’s a tragic figure, his potential derailed by drugs/philandering/emotions, making him an all too human and very sympathetic figure. This is because our minds are programmed to make up stories, to find cause and effect, to indulge in the narrative fallacy. Be careful of that. It will screw up your thinking faster than anything else.

**Just for fun, here’s one of the equations from their model.

This roughly translates as: An individual worker (i)’s output (q)  at time t is equal to the ability of the mean worker (alpha sub zero) plus the specific worker’s innate ability (alpha sub i) plus the set of variables outside the worker-boss interaction (X sub it times capital Beta) plus the ability of an average boss (d sub 0t) divided by team size (N sub jt) to the theta power, where theta is related to public versus private time with the boss plus the ability of the current boss (d sub jt) divided by the team size to theta. This equation relates to the current effect. A longer version of the equation tries to take into account the effect of past bosses and the persistence of boss effects.

***I am reminded of one of the fine haikus inspired by the 1994 World Cup tournament in the US, source sadly lost to me although if anyone remembers it, let me know:
“Run, run, run, run, run
Run, run, run, run, run, run, run
Run, run, pass, shoot, miss.”

Jeff Sullivan, esteemed overlord of the Community blog, wrote a fun article this past week looking at how the average schlub like you or me would produce if given an opportunity to bat and chose to literally never swing. Pitchers, as it turns out, are surprisingly fallible when it comes to striking out realistic simulacrums of hitters, and the expected production would be nonzero by a hair. What a couple people claimed in the comments was that they could be better than Jeff’s predicted .000/.073/.000, that they could swing blindly a couple times and occasionally get on base, somehow. Maybe that was you! I call shenanigans. Swinging obviously leads to negative outcomes as well, and I think the decreased chance of a walk would outweigh the nonzero chance of a hit. I took Jeff’s frequently given advice (if you want to know more, do the research yourself) and decided to see what would happen if you did decide to swing sometimes.

Throughout this, I’m going to follow a fairly similar methodology to Jeff and use a lot of his numbers whenever possible, so we’ll assume pitchers have the same abilities to throw strikes he gave. It’s true that it might change a bit if they knew what you were planning, but Jeff assumed the pitchers thought you were at least thinking about swinging, so it’s not too big a stretch. To extend the conclusions, we have to decide a few things — when you swing, how likely you are to make contact, and what happens when you do.

In making these assumptions, I’m trying to channel what you or I, a reader of FanGraphs, would do in the batter’s box. If I decided to swing ever, it would be just that, a conscious decision, one I would probably have to make before the ball ever left the pitcher’s hand. There is no way I have the ability to determine if a pitch is a ball or a strike or at all hittable while it’s approaching me rapidly. Therefore, in our scenario, you have a predetermined chance of swinging at any pitch, ball or strike. We’ll call that swing rate, and it depends entirely on how aggressive you’re feeling.

Now we need contact percentages, and for the next few sections, I’m again going to follow Jeff’s lead by looking for some of the historically worst contact percentages ever, and adjust those down somewhat. Looking over the last six years for players with at least 20 plate appearances, there are some pretty terrible numbers for both in-zone and out-of-zone contact rates. Sean West of the Marlins and Alex Wood of the Braves both have O-Contact rates of 0.00%. The lowest non-zero rate belongs to Sean Gallagher, at 14.3%. The lowest from a position player is that of Reid Gorecki, outfielder for the Braves, at 18.8%. It seems fairly reasonable to set your O-Contact% at 10%. Z-Contact% is better, but we see some of the same names. The lowest is Rick VandenHurk, 36.8%. Sean Gallagher ranks third-worst here as well, at 55.6%. Mike Costanzo is the worst position player at 59.3%, though good ol’ Reid Gorecki isn’t far behind, the fifth-worst position player at 68.6%. 50% seems like a good round number to choose for Z-Contact%.

So now we know when you swing, and whether or not you make contact. What happens then? There are 10 players with at least 20 PAs who have a BABIP of .000, so it’s presumably possible (maybe even likely) to ground out every time we even manage to make contact. Brandon McCarthy is the lowest-nonzero rate, at .037, or one hit in the 27 times he’s put the ball in play. The lowest position player is Oswaldo Navarro, shortstop, at .063. We’ll be generous, and assume some of these awful numbers are bad luck and need to be duly regressed, and put your true-talent BABIP at .050.

The floor for your hypothetical power abilities is even lower. Lots of position players have .000 ISO, with all of Luis Durango’s 19 career hits in 74 PAs (the largest sample size among players at .000) being singles. That’s boring, however. We want at least some chance for extra bases. ISO, however, measures extra bases per at-bat, when really what we want is extra bases conditional on the ball being a hit. We can safely set that pretty low as well. Johnny Cueto has a surprising number of PAs, at 340, and the lowest nonzero ISO at .004. Of his 26 hits, 25 were singles, with one double. There are lots of similarly bad numbers, so we’ll put your chance at extra bases at .02, or once per every fifty hits. Sounds about right. They’ll all be doubles — no way you’re hitting the ball hard enough for a triple or dinger, inside-the-park or otherwise.

Now it’s time to plug and chug. (Before continuing, an aside: we’re obviously assuming unrealistic things here, like that our BABIP will be the same on pitches in the zone and on those not, and that the probabilities will be the same regardless of the count, but we’re also assuming that you’re in a major-league game, so let’s not quibble over realism.) Continuing to shamelessly appropriate Jeff’s numbers and methodology, when we step up to the plate, there’s about a .24% chance we’ll be hit in the at-bat and automatically reach base. Nice! Jeff set an expected strike-rate of 70%, and we’ll use that as well. If we didn’t swing, we walked or were hit enough for an OBP of .073. How does swinging sometimes change your production?

Oof. Not well, is the answer. You can see that while BA and SLG increase when you swing more, it’s not nearly enough to cover the increased strikes and in-play outs, and reasonable swing rates in the 25-60 percent range cause lead to some even worse wOBAs in the .020s and .030s. Interestingly, the minimum wOBA comes right around a swing rate of 45%, after which it is better to swing more no matter what your rate is, until you’re deciding to swing before literally every single pitch. They should probably learn your strategy fairly quickly, but until they do, your OBP is about .040 (you get on base once every 25 at-bats!) and your wOBA is about .036. For comparison, Jon Lester (my go-to terribly-hitting pitcher) has a lifetime OBP of .030 and a wOBA of .021.

How good would you have to be for swinging sometimes to be better than doing nothing? It doesn’t take a lot. If you can make slightly more solid contact, and we up your BABIP to .150 and the chance of a double given a hit to 15%, swinging more is a very good thing, with your wOBA topping out at .110 (!!!) when you swing 100% of the time. To be fair, though, while this is a small increase in magnitude, it’s better contact than a lot of pitchers make, so I think the original assumptions are probably closer to the truth.

So now you know. If you’re ever dropped into a baseball game somehow, remember Eddie Gaedel, and keep the bat firmly fixed on your shoulder. Because while you would be terrible, really really terrible, it would be even worse if you for some second you forgot how terrible you were and tried swinging.

PS — I put together a whole spreadsheet where you can mess with all these numbers and see how it changes the results, and if that’s something people would be interested in I could provide a link so you can make assumptions of your own.

As the second half of the brain trust known as “The ‘I’ in Team is in the A-Hole,” I am tasked with the responsibility of handling the analytic side of our fantasy baseball team. In my continuing preparation for the March draft, I stumbled across an interesting question: What is the best way to prepare for a fantasy draft in a keeper-style league?

From the outside, it is a very easy question to answer. The convenient availability of projections, rankings, and draft cheat-sheets provide a nice guide to determine which players to select. How would you prepare for the draft if the top 150 players were removed from the pool of players before the draft began? This is a challenge that team owners in keeper leagues face. In my league, assuming every owner keeps the maximum of 10 players, the top 100 players will be removed from the talent pool. Drafting in order and ranked by WAR, Starlin Castro would be the first overall pick in the draft. Although this is an exaggeration, it still proves the point I am trying to make. Keeper-league owners need to prepare for the draft differently than traditional redraft-league owners.

I like the umbrella approach WAR has to describing player value. I understand that rotisserie points are dependent on production. WAR provides a measure of general offensive production (wRAA), their ability to advance when on base (UBR and wSB), and their positional value on the field which, theoretically, translates to more playing time (UZR). To test the fantasy relevancy of WAR, I compared 2014 Steamer WAR projections to ESPN’s 2013 end of the year Player Rater. Although not significant, there was a 0.631 correlation. For the most part, the Player Rater aligned fairly well with the player’s projected WAR.

I then took the top 40 players ranked by WAR for positions 2, 3, 4, 5, 6, and DH and selected the top 100 players for the outfield. The total sample was 283 players. On average, Third Base (3B) is the most productive position (2.587) followed by Catcher (C) (2.487) and Outfield (OF) (2.376). To eliminate Mike Trout’s 9 WAR, I broke the position samples into four tiers. Tier 1 included the elite players (Top 25%) with subsequent tiers composed of lesser ranked players. Tier 1 players averaged a 3.88 WAR across the seven measured positions. When breaking the players into performance tiers, we see a significant decline from Tier 1 to Tier 2 (3.887 to 2.422). This is relevant because the majority of kept players will be from the Tier 1 group and will be ineligible for the draft. This decline continues through the four tiers. Third base represents the largest decline in projected WAR at -3.93.

With the elite players gone, how do you decide which players to select? More importantly, how do you determine depth scarcity without the presence of those elite players? First Base (1B) and C represented the highest average WAR across the remaining three tiers (1.947 and 1.867 respectively). You could argue here that reaching for a 1B in the early rounds is unnecessary because there is potential performance deeper in the draft. Surprisingly, 3B also presents an opportunity for those owners without Miguel Cabrera. As a position, Tier 2 and Tier 3 average 2.88 and 1.9 WAR respectively and represent the highest average WAR for each of the collective tiers.

Where is the position scarcity? If you are looking to draft OF, the data indicates you should plan on taking them early. After eliminating the elite players from the sample, OF averages the second-lowest WAR among the remaining tiers (1.287).

Interestingly enough, the data presents some obvious draft windows. For example, Second Base (2B) has one of the lowest average WAR (1.897); but teams need to select either a Tier 1 or a Tier 2 2B as the performance drop is significant (-3.17). Not only does this represent a decline in performance, but it also indicates an obvious area of position scarcity. Injuries here could be difficult to overcome during the season. The data also shows that there is no need to reach on a C or 1B as there will be decent depth throughout the draft. Tier 4 C averaged 1.23 WAR representing the highest value in Tier 4.

It is critical to consider the value of your available roster spots. In the same way that MLB teams are looking to maximize their available 27 outs, fantasy owners should consider the value of their 25 available roster spots. An owner would need to roster a Tier 2 and a Tier 3 3B to produce comparable value to a Tier 1 3B. Maximization of available roster spots will provide the owner with the flexibility needed to adapt to the 26-week season.

As we all know from the movie Bull Durham, strikeouts are fascist and groundballs are democratic.  So, I want to set out to find the most democratic pitchers and the most fascist pitchers out there.  Luckily, FanGraphs offers a custom leaderboard page that includes batted-ball data.

I set the filters to allow a K/9 rate of 5 or less in a game, a groundball percentage of 50% or greater, and a minimum innings-pitched threshold of 500 innings from 2002-2013.  I realize that five strikeouts a game is kind of arbitrary but I wanted to focus on pitchers who were striking out a batter about every two innings.  You can see the leaderboard for the most democratic pitchers from 2002-2013.

Based on that leaderboard, Aaron Cook should be considered the most democratic pitcher of the twelve-year span, based on his 3.7 K/9 and 57.5% groundball rate.  So, there’s that on his mantle.  Although, I still get confused trying to figure out how Cook was successful. Some other options for most democratic pitcher could be Jake Westbrook and Chien-Ming Wang.  Westbrook had a higher K/9 than Cook but also a higher GB%.  Wang was only slightly higher than Cook on his K/9 but induced groundballs at a slightly higher rate, too.  If you want to say Wang should be more democratic than Cook, far be it from me to stop you.

But, I also wanted to look at pitchers who have had democratic seasons during the span.  So, I created another leaderboard.  Not surprisingly, Cook appears near the top of the leaderboard in terms of value for his democratic season.  Tim Hudson had the most valuable democratic season in 2004, having an fWAR of 4.9.  The difference between 4.9 and 4.5 fWAR, that Cook put up in 2008 is probably not statistically significant.  I feel confident in saying that Aaron Cook is the most democratic pitcher for which we have comprehensive data.

On the flip side of this, I wanted to see who would be considered the most fascist pitchers for which we have data.  To set the parameters, I chose a K/9 of greater than or equal to 10.8 (represents 40% of 27, or how many outs a pitcher can get in a ball game) and a GB% of less than 40% with the same innings requirement as before.  The leaderboard can be found here.

Based on the leaderboard, there are only two fascist pitchers out there: Octavio Dotel and Carlos Marmol.  For some baseball fans, they are essentially the same pitcher and based on the rate stats it is hard to tell them apart.  Dotel was more valuable somehow being able to register a lower FIP than Marmol and pitching about 70 innings more.  So, Dotel is probably a little more fascist based on this stat.

Looking at individual seasons, I chose the same rates but with a minimum of 60 innings pitched.  The leaderboard for individual seasons has a handful of seasons registered by starting pitchers.  By and large, though, these types of seasons are usually only put up by relief pitchers.  Max Scherzer, Rich Harden, and Oliver Perez had more or less the same season in terms of value.  But Rich Harden’s season in 2008 is absolutely stunning.  Look at that low GB%, look how fascist it is.  There are a couple of pitchers on the individual-season list who don’t meet the 500-innings mark in Aroldis Chapman and Kenley Jansen who could also be in the running for most fascist pitchers.  Harden’s individual season was the most fascist, for the purpose of this exercise.  It seems unlikely that a starting pitcher can survive with such a low GB% or keep up such a high K/9 over the course of his career, or a number of seasons.

After the 2011 season I asked, and attempted to answer, the question, “what is an ace”?

It’s time to do that again.

Ok. While Kershaw is the aciest of aces right now, that’s not really the answer that we’re looking for.

I certainly don’t claim to be the first person to do something like this, nor am I the most rigorous, but I think it’s good to take a look at things like this every now and then just to reset our baselines.

What I did was to take the average of every starter’s fWAR and RA-9 WAR. Then I used that number to group pitchers into groups of (roughly) 30 — 30 aces, 30 number 2’s, etc. Then, I looked at the average performance of the pitchers in each group.

Here’s what I found:

There’s a couple of interesting things to note.

One is that the best 30 pitchers in baseball are, far and away, the best group. They strike out the most hitters, they walk the fewest hitters, they give up the fewest home runs, they have the lowest BABIP, they’re the best. That’s not surprising when guys like the above-pictured Kershaw, Cliff Lee, Max Scherzer, Justin Verlander, Matt Harvey and Yu Darvish are in the ranks.

The second interesting thing is how similar the #3, #4 and #5 groups are in terms of performance. Look:

#3 18.2% K, 7.2% BB, 3.85 ERA, 4.06 FIP, 4.04 xFIP, 4.13 SIERA
#4 18.7% K, 8.2% BB, 3.89 ERA, 3.86 FIP, 3.96 xFIP, 4.09 SIERA
#5 17.4% K, 6.9% BB, 4.26 ERA, 4.09 FIP, 4.02 xFIP, 4.12 SIERA

In many ways, every way other than walks really, #4 starters outperformed #3 starters. Well, in every way except for number of starts and innings. Number-three starters made about seven more starts and pitched almost 50 more innings than #4 starters. Similarly, #5 starters were a little worse than both #3 and #4 starters but what really limited them from producing value was that they made 12 fewer starts and pitched half as many innings as #3 starters.

The third point is similar to the above. Starters not in the top five accounted for more starts and more innings than the best pitchers in baseball. That makes sense when you stop to think about it, there are more bad pitchers than elite ones, but we don’t think about just how important it is for the other starters to make their starts so these guys don’t have to.

As I mentioned when I first did this little exercise after the 2010 season:

Next time your team signs a pitcher with a 10 – 8 record and 3.99 ERA in 160 innings realize just what you are getting. One of the top 100 pitchers in the league.

The numbers are a little different now — now the average #3 is 10 – 9 with a 3.85 ERA in 158 innings — but the point remains the same: the average baseball fan vastly underrates pitcher performance.

Much has been made of the Cardinals’ amazing depth and seeming ability to pull All-Star-caliber players from their minor leagues at will.

In today’s FanGraphs After Dark chat with Paul Swydan I asked what place in the NL Central the Cardinals would finish in were they to be forced to field two separate (but equal) teams in 2014.

Swydan’s answer:

Probably third and fourth. They’re not THAT good.
Maybe even lower than that. It’s an interesting question.

Well, I too thought it was interesting and decided to try to find out.

I looked at the Oliver projections for the Cardinals and tried to divide them into equal teams. Then I did my best (well, my most efficient, it is 9 at night) to divide up playing time equally between both teams. STEAMER projections assume 600 PA’s for all position players so I prorated each player’s WAR projection for the number of PA’s that I estimated (I tried to stick to 600 PA’s for each position – too much work to do otherwise).

For pitchers I used Oliver’s projected number of starts for starters and innings pitched for relievers to make sure that both teams were equal. I didn’t do any prorating for pitchers. I wanted to, but that started to look like more work than I was willing to put in right now — and I was sort of worried that Paul would do his own post on this, so I wanted to beat him to the punch.

There weren’t quite enough players projected for the Cardinals so for the missing positions I just assumed a replacement-level player.

These were the teams and their projected WAR totals that I came up with.

So each team was at about 25 .5 WAR.

How about the rest of the NL Central?

For this I just looked at the STEAMER projections since they already adjust playing time and I didn’t want to have to do it for each team. This is what STEAMER had for the other NL Central teams:

Pirates 34.5 WAR
Reds 30.5 WAR
Brewers 27.6 WAR
Cubs 26.9 WAR

So, our Cardinals teams look like they’d finish just behind the rest of the NL Central, but it’s close enough that we can say that the Cardinals might literally be twice as good as the Cubs and Brewers.

I was recently involved in an online discussion of the Prince Fielder/Ian Kinsler trade and the signing of Jhonny Peralta by the St. Louis Cardinals. Someone stated that Peralta was no more than a utility infielder who could sometimes hit. I pointed out that, over the last three seasons, Peralta was actually a top-five SS. Someone else stated that Prince, were he to play SS, would also be a top-five SS. I thought that was ridiculous, but decided I’d try to look at it as objectively as possible.

Over the last three seasons, Fielder has 111 batting runs, -18 base running runs, 61 replacement runs and -10 fielding and -37 positional runs for 107 total runs.

If we assume that his batting, base running and overall playing time would stay the same, which is probably an optimistic assumption given the likely additional strain of playing SS instead of 1B, then we only need to adjust his positional and defensive runs.

The positional adjustment is the easiest to adjust. The adjustment for 1B is -12.5 runs per 1350 innings, the adjustment for SS is +7.5 runs per 1350 innings. Fielder’s -37 positional runs represent (-37/-12.5) 3.0 defensive seasons. Three defensive seasons at SS is worth (3 * 7.5) 23 runs.

At this point Fielder at SS is worth 111 batting runs+-18 base running runs+23 positional runs+61 replacement runs. That’s 167 runs all told. That’d make him, by far, the best SS in the league. Troy Tulowitzki has 114 runs.

But we still haven’t factored in Fielder’s defense compared to the average SS. I’m not really sure that we can.

Fielder has been about six runs worse than the average 1B each season of his career. But the average SS is a much better defensive player than the average 1B.

I think it’s safe to assume that Fielder would be the worst defensive SS in baseball.

Since 2002, the UZR era, the worst season by a SS (minimum 650 innings, about half a season) is Dee Gordon’s 2012 season in which UZR says he was worth -27 runs per 1350 innings.

That’s a somewhat amusing comparison. Dee Gordon is listed at 5’11” 160 lbs. Prince is listed at 5’11” 275 lbs. Those are listed weights and I think it’s entirely possible that Prince weighs twice as much as Gordon.

I’m going to go out on a limb as say that Prince would be a worse defensive SS than Gordon. I’d go so far as to say that he would be considerably worse. But how much is considerably?

UZR can be broken down into different components.
Range runs – attempts to measure a player’s range; how many balls he does/doesn’t get to compared to average.
Error runs – attempts to measure how many runs a player saves/costs his team by avoiding/making errors
Double play runs – attempts to measure how many runs a player saves/costs his team by turning/not turning double plays.

I’m going to assume that Fielder would be the worst at all three of the above. So, what would that look like for Fielder’s overall defensive worth at SS?

It’s worth noting here that most of Gordon’s poor UZR was due to making errors, his range and double plays were bad, but not historically bad. His errors were.

The worst SS in terms of double play runs (per 1350 innings) was, go figure, 2012 Dee Gordon at -5 runs per 1350 innings. If we say that Fielder was equally as bad as Gordon, I’ve little doubt he’d be much worse than Gordon, that’d be (3*-5)-15 runs over the 3 seasons.

The worst SS in terms of range runs was, not surprisingly, 2012 Derek Jeter at -17.5 runs per 1350 innings. Anyone think that Fielder has Jeter’s range? I don’t. But if we give Fielder three seasons as poor as Jeters’ 2012 that’s (3*-17.5) -53 runs for 3 seasons.

The worst SS in terms of error runs, bet you guessed that it, was 2012 Dee Gordon at -13 runs per 1350 innings. Again, I think that Dee’s footwork and hands around 2B would be much better than Fielder’s, but if we say that Fielder was as good as Gordon then he’d be worth (3*-13) -39 runs per the three seasons.

If we add all of that up (and remembering that this is-I believe-an optimistic look at Fielder’s possible performance at SS, we get Fielder being (-15-53-39) -107 runs worse than the average SS. Quite a bit worse than Gordon’s -27 runs

Let’s add that to his other performance from above:
111 batting runs, -18 base running runs, -107 fielding runs, 23 positional runs, 61 replacement runs = 71 total runs.

71 total runs between 2011 and 2013 would have put Fielder 12th among major league SS, between Hanley Ramirez (84 runs) and Marco Scutaro (70 runs), and worth about 2.5 WAR per season.

To emphasize again, I think these are the most ridiculously optimistic assumptions that I can present with a straight face. I think it much more likely that Fielder would be a -50 (per 1350 innings) or worse SS were he to play there everyday. Not to mention the additional strain on his body that would decrease his hitting, baserunning, and ability to play every day.