Teams have both success and failure in quite a lot of ways, so I am playing with a way of showing what teams look the most alike.  To do this I have created a percent similar score as follows:

First I pulled team level WAR data split into what I am calling HWAR (position players/hitting) and PWAR (pitching) for all teams from 1947 to 2013.  I then converted each of those numbers into a percent above or below league average for that particular season.  For instance, the 2013 Rangers had 21.5 HWAR/19 HWAR league average minus one to convert to percentage, so they have an HWAR% of 13.1 or 13.1% better than average by cumulative war (actual HWARs above are not rounded in the data so it doesn’t round to 13.2% like it does in the example).  I did that for each team and also a PWAR% for each team in the same manner.

Next I compared each team to each other team with a giant 1610 by 1610 matrix, or a little over 2.5 million team pairs, to see how similar the teams were to each other.  The formula for this was 1/((1+ABS(HWAR%i – HWAR%j))*((1+ABS(PWAR%i-PWAR%j)), which gives a percent similarity based on nominal absolute deviation for each team from each other team multiplied together.  That way the deviations can’t cancel each other out and we are bounded between 0 and 1, and each team compared to itself will yield a similarity score of 100% as you would expect.

From this we can find some fun historic pairs, but also I will add 2014 YTD data and see who the best matches are for current teams and their results.  The two most similar teams out of the 2.5 million+ pairs were the 1999 Cardinals and the 2005 Nationals with a similarity score of 99.9%.  Both were slightly below-average teams.  The Cardinals were 15.5% below average by PWAR% and 9.6% below by HWAR%, and the Nats were 15.6 below and 9.5 below respectively.  That St. Louis team ended up going 75-86 on the season as we would expect from these numbers, but Washington managed to scrape by at an even .500 at 81-81.

On the other end of the spectrum, the least similar teams were the 1998 Braves and the 1979 Athletics.  That was a fantastic Braves team with PWAR 80.7% above league average and HWAR 97.5% above.  Meanwhile, the 1979 A’s were awful at 65% below average in PWAR and 151% below in HWAR, yes they had a negative HWAR as a team which is impressive if you like train wrecks.  These two teams had a similarity score of 11.7%, and their records show it.  That Braves team won 106 games and that A’s team lost 108 games, that is about as far apart as two teams can get.

There are some legitimately useful things I am planning on doing with these scores down the road, but for today I also thought it might be fun to see who is most like the 2014 contenders and how their respective seasons turned out.

The teams in the best probability for the playoffs have the best comps as you would expect with the exception of the Nationals who drew a very mediocre 83 – 79 team as most similar.  Baltimore had the only 100-game winner , but there are plenty of good teams in the mix like the Dodgers comp of a 95-win Expos team.  The different eras prevent us from seeing a ton of playoff outcomes, but none of the comparable teams made it to the World Series.  This year’s lack of any dominant teams might make that an expected outcome, even Buster Olney on the Baseball Tonight podcast today was discussing this very topic.  Of course everyone expected this year’s Detroit team to look like last year’s Royals.

Anyway, this could be a good way to create groups of historical comparisons for teams and the methodology could be broken out more if you want to separate defense, base running, bullpen vs. starters, which could all be done.  How you multiply them together to get appropriate weighting would be the sticky part with that.  It is a simple way to look at teams that had similar outcomes, and WAR allows us to control for ballpark factors and such.  I welcome any comments on other things you think could make it work better.

It’s the bottom of the fourth. No outs. Your beloved Milwaukee Brewers are up to bat trailing the Dodgers 1-0, with Clayton Kershaw on the mound. They’ve picked up two scattered hits and drawn a walk over four innings, but the sentiment in the dugout and the stands seems to read if they haven’t scored yet, chances don’t look so good.

Consider the same situation, now, with one small change. Your Brewers are still down by a run. It’s still the bottom of the fourth. Kershaw is still dealing. But it’s 2-1 Los Angeles this time. Milwaukee has still only gotten two hits and drawn a single walk, but the timing has worked out such that a run scored. By the numbers, things are almost exactly the same. No question about it. The sentiment, though, is certainly different. We’ve broken through once already, think the players, manager, and fans. We can do it again. Well, of course the Brewers can do it again. But, statistically speaking, will they? That is: when trailing by one run as they enter a half-inning, is a team more likely to come back in a non-shutout than in a game in which they haven’t yet scored?

The answer is “yes,” although only by what initially appears to be a small margin. In 2013, 5705 half-innings began with the batting team trailing by a run. 11.4% (651) of those half-innings ended with the batting team tied or in the lead. The same year, 2915 half-innings began with the batting team trailing specifically by the score of 1 to 0. 11.1% (324) of those ended in a lead change or tie.

At first glance, a 0.3% difference between odds of scoring when down by a run versus the specific case of being down 1-0 seems minor. And it is, really. For years with complete-season data available since 1871, the percent of half-innings started where it’s a one-run game and the losing team up to bat which resulted in a lead change or tie (let’s call this %ORLC) averages out to 11.5% ± 1.3% (1 σ). The subset of these in which the batting team was being shutout (let’s call this %ORSLC) has an average of 10.6% ± 1.1% (1 σ). Middle-school statistics will tell you that while, yes, %ORSLC is on average nearly a percent lower than %ORLC, they fall within a standard deviation of each other and, thus, their difference is not statistically significant.

That’s true. But baseball isn’t middle-school statistics and two subsets whose error ranges overlap are not for all practical purposes equal. Quite remarkably, %ORLC has exceeded %ORSLC for each consecutive season of Major League Baseball since 1977 (when %ORSLC was 0.2% higher) and every year since 1871 except for five seasons (out of the 111 years of complete-season data that were available).

That is: in 106 out of the last 111 seasons for which box scores have been logged every game, a batting team behind in a one-run ballgame has successfully erased the deficit more often when not trailing 1-0. The margin isn’t huge, of course, but the trend is meaningful.

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Above: Percentage of one-run game situations and specific 1-0 game situations (%ORLC and %ORSLC, respectively) in which the team losing scores to tie or take the lead

After all, baseball is a game of small but meaningful margins. The 111-year average relative difference between these two metrics (10.6% vs 11.5%) is proportional to a .277 batting average versus .300, or 89 wins in a 162-game season instead of 97. The latter is perhaps a more relevant comparison, since it is gaining (and maintaining) a lead that is crucial to winning games.

Among teams in 2013, however, these differences aren’t so marginal. In %ORLC (percentage of half-innings in which a team trailing by a run ties it up or takes the lead) the Royals finished first at 16.7% and the Cubs finished last at 6.5%. In %ORSLC (same stat but for the score 1-0), the Rays finished first at 16.7% (same number, coincidentally) and the Red Sox finished last at 4.9%. Considering the Royals didn’t make the playoffs in 2013 and the Red Sox won the World Series, I wouldn’t use %ORLC and %ORSLC as indicators of a team’s ultimate success unless you’re looking to lose a lot of money in Vegas.

While one could theorize for hours on the meaning and utility of each made-up statistic, it sure doesn’t seem like %ORLC and %ORSLC are indicative of much on a team-by-team basis. But that doesn’t mean they’re useless. Let’s go back to the long-term trend of %ORLC and %ORSLC, where the former was higher than the latter 106 out of 111 times.

Some underlying process, it would seem, must be responsible for this impressive stat. If we are to believe that teams truly underperform, ever so slightly, when they’re losing 1-0 due only to the fact that they’re being shut out, shouldn’t we able to see the effect of psychology on performance somewhere else?

As it turns out, you don’t have to look far. Let’s consider the general situation of a team coming up to bat down by a run (not only the specifically 1-0 case), which is colloquially termed a “one-run game.” We’ll abbreviate any instance of this (a trailing team coming to bat in any half-inning) as OR. Now this situation could happen at any point in a game. A visiting team leads off with a run in the top of the 1st, the home team comes up to bat – that’s an OR. It’s all tied-up in the top of the 13th, the third baseman slugs a solo shot to left, three outs are recorded, the home team steps up the plate with one chance to stay alive – that’s an OR. So, in what inning on average does an OR occur?

In 2013, the answer was the 4.95th inning. In 2012 and also for the last 111 years of available records, the 4.91st inning. Baseball amazes us once again with its year-to-year consistency in obscure statistics. But this obscure stat isn’t all that meaningful on its own. Okay, so most one-run situations occur near the 5th inning – so what?

Well, let’s take a look now at the average inning in which a team scored in an OR to tie or take the lead. We’ll call this a one-run game situation where the lead changes, or ORLC. In 2013, of all the instances of ORLCs, the average time they occurred was the 5.18th inning. In 2012, the 5.10th inning. And for the same 111 seasons of recorded game data, the 5.20th inning. Once again, we see a marginal but nonetheless compelling deviation from the average, just as we saw with %ORSLC. Teams score in one-run situations about a third of an inning later than the one-run situations tend to occur themselves. That may not seem like a whole lot, but consider that in our 111-season dataset only two years – 1902 and 1912 – saw earlier ORLCs than ORs on average. Just two years in one-hundred eleven.

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Above: Average innings of occurrence for one-run game situations (OR) and one-run game situations in which the trailing team scores to tie or take the lead (ORLC)

So what’s going on? I like to think of average ORLC minus average OR as a league-wide statistic for urgency. Consider the following: if the inning number had no effect on the performance of a trailing team in a one-run situation, then we would see roughly the same average inning of occurrence for both OR and ORLC. Out of 111 years, we’d expect to see about 55 years in which OR occurred earlier on average than ORLC and around 55 in which it didn’t. But we don’t see this at all, which strongly suggests that inning number has an effect on how a team does at the plate when down by a run. This is the urgency statistic. It describes a trend that has rung true for the past 101 consecutive seasons of Major League Baseball – when time is running out and the 9th inning is rapidly approaching, teams in close games get their acts together and produce runs. Not every time, of course, but we’re speaking in averages of massive sample sizes here.

So, while your Brewers are likely to fare worse trailing Kershaw and the Dodgers 1-0 than 2-1, take solace in the fact that it’s the fourth inning. Statistically speaking, they’ll have a better chance breaking through as the game goes on and their need for a run becomes more urgent. The effect of team psychology has left its imprint on the records of baseball games since the sport’s earliest days.

As I noted here on August 15, major league batters are being hit by pitches at rates not seen in over a century (measured by HBP/game). I offered data illustrating this but was at a loss explaining it. Fortunately, I spent the following weekend at the Saber Seminar in Boston, surrounded by a bunch of really smart people, so I posed the question to them.

To be clear, everyone was surprised by the conclusion. Through Sunday’s games, there were 67.4 hit batters per 100 games in 2014. That’s the highest rate since 2001-2008 which, in turn, is the highest since the two leagues were formed in 1901. (Note that these numbers are different from the ones in my original post. When I downloaded league totals from FanGraphs, I hadn’t realized that Games referred to player games, not team games. So I was using a denominator that was too large. The conclusions still hold. I’ve updated the figures in a comment to the August 15 post.) If you didn’t notice this spike in hit batters, join the club. This appears to be an entirely under-the-radar trend.

Asking around, I got several possible explanations. Dave Cameron, FanGraphs managing editor, suggested that since PITCHf/x has clearly demonstrated that left-handed hitters suffer from an elongated strike zone on the outside part of the plate, lefties might be setting up closer to home in order to reach those outside “strikes.” That would make them more likely to be hit by inside pitches.

To test this, I looked at data from 2010-13, when there were 63.8 hit batters per 100 games, and compared them to 1980-83, when there were just 32.6. Switching from HBP/100 games to HBP/1000 plate appearances (since games contain a combination of left- and right-handed batters), the hit batter rate went from 4.3 in 1980-83 to 8.3 in 2010-2013. Right handed hitters got hit at a rate of 4.6 per 1000 plate appearances in 1980-83 and 9.0 in 2010-13, an increase of 95%. For lefties, the HBP rate went from 3.8 to 7.3, and increase of…95%. Exactly the same. Handedness hasn’t been an issue.

Former major league pitcher Brian Bannister suggested that I correlate HBP rates to measures of power. He noted that he didn’t like to come inside on sluggers, for fear that they’d pull the pitch down the line and into the bleachers. I thought this was a sharp, counter-intuitive insight: A rash of longballs makes pitchers work away rather than come inside. With offense in retreat in recent years, pitchers are more willing to pitch inside, and when they miss, the batter gets hit.

I looked at three measures of power: home runs per plate appearance, slugging percentage, and isolated power. I correlated these metrics to hit batters per game for the period 1980-2013. If Brian’s hypothesis is correct, there should be a negative correlation–as power increases, hit batters decrease. However, the opposite was true: 0.84 correlation coefficient between hit batters per game and homers per plate appearance, 0.55 for slugging percentage, 0.65 for isolated power. Maybe my endpoints were wrong? I checked 1970-2013 and got pretty much the same results: Correlation coefficients of 0.84 for HR/PA, 0.66 for SLG, 0.73 for ISO. I was ready to think that maybe hit batters are a result of more power, not less, but then I picked 1990-2013. At least during those 14 years the correlations were weaker, coming in at 0.82 for HR/PA but 0.25 for SLG and 0.43 for ISO. That’s still consistent with the observation that hit batters have remained high in the post-PED era. I don’t see a strong case for fingering the long ball as a cause for hit batsmen, one way or the other.

SiriusXM radio host Mike Ferrin thought we may be seeing a cultural shift of sorts. In college ball, he pointed out, batters view getting hit as an on-base weapon. Might an influx of college players be driving up HBP rates?

Unfortunately, neither the FanGraphs Leaders board nor the Baseball Reference Play Index have college vs. non-college splits, so I looked at the most-plunked batters in 2013 and 1983. In 1983, players with four or more HBP represented the top 53 overall and collectively comprised 274 of 717 HBP that year, or 38%. Of those 53, 30 (56%) attended a secondary school in the US. (I am going to use “attended college” instead of “attended secondary school” going forward, but I mean players who went on for any schooling, including junior college, following high school in the US.) They were hit 10.2 times per 1,000 plate appearances. Players who didn’t go to college were hit 10.4 times per 1,000 plate appearances. That’s our baseline: No evidence of college kids leaning into pitches the year “Every Breath You Take” and “Billie Jean” were top hits.

Now, 2013. There were 15% more teams than in 1983. As it happens, there were 61 hitters with seven or more HBPs in 2013, and 61/53 = 1.15, so 61 is the appropriate sample size for consistency. Those 61 batters were hit 587 times, 38% of all HBP, just like our sample for 1983. Here are the relevant metrics:

• Percentage of most-hit players who attended college: 57% (30 of 53) in 1983, 49% (30 of 61) in 2013
• Percentage of HBPs by most-hit players that were players who attended college: 56% (167 of 298) in 1983, 47% (275 of 587) in 2013
• HBP per 1,000 plate appearances, all most-hit players: 10.4 in 1983, 18.2 in 2013
• HBP per 1,000 plate appearances, most-hit players who attended college: 10.2 in 1983, 18.5 in 2013
• HBP per 1,000 plate appearances, most-hit players who didn’t attend college: 10.8 in 1983, 17.9 in 2013

Mike has a point. College players appear to be getting hit more, relative to other hitters, than they were in the past. The rate of HBP per 1,000 plate appearances increased 82% over 30 years for batters who went to college. However, the HBP rate for batters who didn’t go to college was up 65%, which is also pretty dramatic. And the list of HBP leaders has more players who didn’t go to college than in 1983. So while college kids may be bringing a lean-into-the-pitch ethic to the plate, there is still strong evidence that players who didn’t attend college are getting hit more, and the limited data I used don’t indicate that college kids are comprising a growing percentage of plate appearances.

Some of the commenters on my post from the 15th suggested that maybe HBPs are up because pitchers are throwing harder, giving batters less of an opportunity to get out of the way of an errant delivery. Per FanGraph’s PITCHf/x data, average fastball velocity has climbed from around 91 mph in 2007-9 to 92 mph in 2013-14. That’s a pretty tiny difference from a hitter’s perspective (about five milliseconds, or 0.005 seconds, over 60.5 feet), but it’s something. I’m not ruling it out.

Last Wednesday on the Effectively Wild podcast, Baseball Prospectus’s Sam Miller noted that the rate of batters reaching base via catcher’s interference is near all-time highs. (And you thought hit batsmen per 1000 plate appearances was obscure…) He hypothesized that one of the reasons is that batters are standing further back in the batter’s box in order to get extra time — maybe like five milliseconds? — to identify and swing at an incoming pitch. By setting up deeper in the box, batters increase the possibility that their bat may hit the catcher’s glove at the end of their swing, drawing the catcher’s interference call. If that’s correct, wouldn’t moving back also give pitches that break horizontally — two-seam fastballs, sliders, cut fastballs, some changeups — more time to drift into the hitter? It makes sense!

Unfortunately, the numbers don’t back this up. The correlation coefficient between catcher’s interference and hit by pitches is 0.00 since 1962 (expansion in both leagues), -0.10 since 1969 (divisional play), and 0.07 since 1994 (three divisions per league). That doesn’t necessarily mean that the increase in hit batsmen isn’t caused by batters positioning themselves toward the back of the batter’s box, but it does say that whatever’s driving catcher’s interference isn’t the same thing that’s driving hit batters.

So basically I’m back to where I was going into the Saber Seminar. We’re seeing batters hit by pitches at rates not seen in a century. This change has not been widely reported, and I haven’t identified an obvious underlying cause. After talking to people at the Seminar, I still don’t have a great explanation. It could have a little bit to do with fastball velocity, or batter positioning, or players who went to college being willing to get plunked. But I haven’t identified a clear reason thus far. Even with smart guys helping me.

I think I might be the biggest fan of the world of the Ten-Pitch Walk.  I don’t know why, but I get overly excited when I see a player really battle for a long time, against everything the pitcher has, only to win the battle through patience.  Perhaps it’s because it’s so contrary to the spirit of what’s actually exciting about baseball; seeing players run around and field a batted ball.  It’s wholly a battle of attrition.  It’s the baseball equivalent of watching somebody run a marathon; you may not think the act itself is exciting, but it’s certainly an impressive feat in a vacuum.

So this has also lead to a fascination with pitches seen per plate appearance.  I’ve long wondered if certain teams place an emphasis on teaching their players to see more pitches per plate appearance.  It seems fairly self-evident that seeing more pitches is, in a microcosm, better than seeing fewer pitches.  You tire the pitcher out quicker, you see more data for your next at-bat to work with, and you give your team a chance to see what the pitcher has, and how he’ll react in different situations.  I hypothesized, purely based on colloquial wisdom, that the A’s would be good at this and the Blue Jays would be bad at this.  That’s not to say that one approach is better than the other, but just that some teams seem more patient than others.

Fortunately, FanGraphs has data available per hitter as to how many pitches they see.  I pulled that data out and found out each player’s average pitch per at bat since the year 2003 (the earliest we have this data, from what I can tell) and restricted the findings to active players only.  Then I ran some regressions to see if there was any correlation between pitches per at bat and useful batting stats.  Here’s what I found:

We see a slightly positive correlation between P/PA and wOBA.  It’s not really anything to write home about, but it’s more than negative.  It doesn’t seem immediately that seeing more pitches relates heavily to overall performance at the plate.  What about on base percentage?

Slightly better here, but still not great.  Seeing more pitches does have a little more correlation to getting on base, but there are plenty of aggressive swingers that don’t follow that model, so it means the correlation is loose at best.  What if we talk just about taking walks?

Here we have a real correlation.  .59 is a fairly strong correlation, and that makes sense.  The more pitches you see, the more likely you are to take a walk.  If you can successfully foul off anything in the strike zone, you will eventually walk (or the pitcher will die of exhaustion, either way, you win).  This is reasonably useful.  If you’re trying to find a way to make your team walk more, maybe you can invest in some players that see more pitches per plate appearance than normal.  This strong of a correlation makes me think about strikeout percentage too, though, because every pitch you foul off makes you closer (or just one whiff away) from striking out.

There is a positive correlation here, but not nearly as strong as between BB% and P/PA.  It’s stronger than the other useful stats like wOBA, but it’s interesting to know that seeing more pitches relates much more strongly to taking a walk than it is to striking out, at least on a grand scale.  There is some research to be done here to see what the odds are of a plate appearance as the pitch count increases, but I’ll leave that for another day.  My next thought was to see if there are, in fact, any teams that are better at this than other teams.  Here’s what we’ve got on a team level:

Well, my original hypotheses were not great ones.  The A’s and the Blue Jays, at 11 and 13, are both decidedly middle of the road teams.  I find it most fun in times like this to look at the extremes; in this case, the Red Sox and the Brewers.  The difference in pitches seen per plate appearance between these two teams is 0.38.  That may seem small, but it adds up.  If we assume the average pitcher faces 4 batters per inning, that’s an additional 1.5 pitches per inning, and 9 pitches by the end of the sixth, just purely by the nature of the hitters.  In a tightly contested contest, that may mean the difference between getting to the bullpen in the 7th rather than the 8th, or even the 7th rather than the 6th.

It should be noted that I limited this data set to 2014 (in contrast to the earlier data which was 2003 onwards) just so we could get a realistic look at roster construction, and to see if any teams are, right now, putting any particular emphasis in this area. The BoSox are carried by the very patient eye of Mike Napoli (4.51 P/PA), but hurt by the rather hacky eye of AJ Pierzynski (3.42 P/PA). Even on one team, that’s more than a pitch per plate appearance, which is pretty startling. The Brewers don’t have nearly the same difference; their best is Mark Reynolds with 4.04 P/PA and their worst is Jean Segura with 3.42 P/PA. As an aside, Chone Figgins is by far the best in this with a whopping 4.99 P/PA, though it was in just 76 PA. Kevin Frandsen brings up the rear with 3.16 P/PA in 189 PA. A lineup of all Mike Napoli’s would see 24.3 more pitches than a lineup of Kevin Frandsens before the leadoff Napoli even comes up a third time. I would feel bad for that pitcher.

The talk about teams possibly emphasizing this data made me wonder if I could make a huge difference if I compiled a team solely to do this; just make sure the pitchers throw a ton of pitches.  With that, I present to you the 2014 All-Stars and Not-So-All-Stars in this area, with a PA minimum thrown in to eliminate Figgins-like outliers:

Despite the fact that there isn’t a strong correlation between wOBA and P/PA directly, it’s worth noting that the P/PA All-Stars are significantly better than the Not-So-All-Stars. Their difference in wOBA is .328 as compared to .314. The Not-So-All-Stars certainly present a fine lineup though; the All-Stars just have the benefit of having Mike Trout in their lineup. It’s nice to know that this is one other area that Mike Trout simply is amazing at, confirming the obvious. The All-Stars have a collective P/PA of 4.26, while their counterparts sit down at 3.43. That’s .83 pitches per plate appearance, which over the course of two turns through the lineup is 14.94 pitches; that’s definitely something notable.

So, it appears this is a demonstrable skill with some value, though not a ton. We can see that some teams are better at this than others, and we see some positive benefit from this, most notably in walk rate. While we see plenty of players on both sides of the scale who are excellent ballplayers, the data does seem to suggest that seeing more pitches is better than not doing so, though only marginally on a league wide scale. When we isolate leaders in this area vs. those more aggressive, we can see some startling differences though, suggesting that perhaps there is an advantage to be gained here.

I started writing this post prior to the trade deadline; viewed through this lens, the Lester-and-Gomes-for-Cespedes trade makes even more sense for the A’s than it already did, especially considering their parallel acquisition of Sam Fuld from the Twins.

The A’s are ahead of the curve again.  This time it’s not just about better overall player evaluation (concentrating on certain metrics that other teams undervalue), but about building a roster that maximizes each player’s skill set to get the most out of the talent on your roster.  I wrote a while back that WAR is not the be-all, end-all of player evaluation, emphasizing that there is more to Wins than WAR.

WAR is great at certain things; it’s useful to remove factors that are outside a player’s immediate control: park factors, sequencing, etc.; it’s useful for comparing players across eras by controlling for run scoring environments and translating Runs to Wins; it’s also useful because it encompasses multiple aspects of a player’s skill set (hitting, defense, baserunning), and uses the same units (Runs and Wins) to combine these into a single number.  It’s a nice package.

But if I’m a GM, I don’t evaluate each player in a vacuum.  I want to know how he fits into my system, my lineup, my park, etc.  A given player will bring different value to different teams.  Some examples:

• Certain players might be more tailored to certain parks based on their offensive profile.  A contact hitter who hits a lot of infield singles and steals a lot of bases isn’t worth as much (compared to a team playing in league-average conditions) to a team filled with roided-up sluggers playing in pre-humidor Coors field – the value of those stolen bases and infield singles just isn’t as high.  WAR does normalize for park factors, but it assumes all players are affected by a given park equally, which on its face isn’t true.
• A player’s contribution varies based on how his team uses him.  If a team platoons a player so that he often has the advantage, his offensive contribution (per plate appearance) will be increased, whereas if he faces a more standard distribution of pitchers, his contribution would be lower.  Likewise if he plays a position he’s not as used to for the good of the team, his own contribution (as measured by WAR) will be less than if he plays his primary position.
• Likewise, defensive versatility has value to a team.  A player who can play multiple positions allows his team more flexibility in roster construction and in-game management; setting the daily lineup, platooning, and late-game substitutions (matchups when pinch-hitting, or defensive replacements – especially double-switches in the NL).

As a GM, you don’t just add up each player’s projected WAR (and add in the replacement-level constant) and say that’s how many you project to win that year.  There are all kinds of interrelated variables at play that will determine how your team performs.

The A’s are the epitome of this philosophy and appear to be better at this optimization of roster construction.  They’ve loaded up on defensively versatile players with outsized platoon splits and are the king of the platoon.  They’ve started doing this in the last few years, and this year even more so.  Take a look at MLB averages for platoon splits as compared to the A’s:

League:

A’s:

We notice two things: first, the A’s splits are a bit wider than the league splits: their righties hit better against lefties by about the same split as righties league-wide, but their lefties really hammer righties: a .171 OPS split for A’s lefties, as compared to a 0.068 split for lefties league-wide.  They’ve made a conscious effort to go after this style of player.  Second is the distribution of plate appearances: the average team gets 55% of its plate appearances with the platoon advantage.  The A’s get 70% of their plate appearances with the platoon advantage.  They’ve constructed their roster in such a way that they can alter their day-to-day lineup as much as possible to maximize the platoon advantage.

What allows the A’s to do this?  Defensive versatility (and the DH).  They’ve got guys like Brandon Moss playing LF/RF/1B/DH; Craig Gentry playing all OF spots; Stephen Vogt playing RF/C/1B; Alberto Callaspo, John Jaso, Josh Donaldson, and Bud Norris dividing time between DH/1B/3B/C; and so on.  All this versatility allows them to mix and match their lineup to get as many plate appearances with the platoon advantage as possible.  And, by not being pulled down by having any full-time DH, they get additional flexibility.

Cespedes didn’t really fit in with this philosophy.  Nearly all his appearances came in LF – 343 PAs, compared to 17 as a CF and 69 as a DH.  With the exception of Donaldson (423 PAs as 3B), he had the highest concentration of PAs at a single position.  The next-highest was Crisp, a switch-hitter, with 306 PAs as a CF.  Everyone else is playing all over the field.

Cespedes has a bit of a platoon split (0.844 OPS vs. 0.765), but not as much as other A’s like Reddick (0.843 vs. 0.398 this year), Donaldson (1.098 vs. 0.704), or Norris (1.031 vs. 0.771).  Gomes’ platoon split: 0.875 vs. 0.722.

So maybe the A’s think there isn’t that big a difference between Gomes and Cespedes, especially considering that Cespedes’ defense can be partially replaced by Fuld’s, his performance against lefties can be replaced by Gomes, and his performance against righties can be replaced by the left-handed Vogt, who stands to get more appearances in LF now.  If they play their cards right, Fuld/Gomes/Vogt is a better player than Cespedes.

The A’s appear to have a leg up on the competition.  Rather than evaluating players in a vacuum and estimating “How many wins we will get if we add player X and remove player Y?”, they’re looking at “What does our lineup look like with player X?”  “How will his presence affect the number of plate appearances players A, B, C, and D get (with platooning taken into account)?”  “How will our various defensive alignments look?”  “How does his presence affect the availability of late-game pinch-hit and defensive replacement options?”  And for each of those questions, they boil it down to the impact on expected runs, expected runs allowed, and expected wins.  They’re all-in for this year, and they’re pulling out all the stops to optimize their lineup.

Next up, I want to look at whether there are any signs of the A’s trying to get a similar edge based on:

• Park factors – targeting players who fit in with their park
• Clutch hitting ability; the A’s lead the league in the split between hitting with runners on base vs. with the bases empty; why?  Is it just luck, or have they found a way to get players who are better at hitting with runners on base?

During spring training I was watching the Mariners, harassing Jesus Montero falling down while fielding and in awe of Robinson Cano’s crazy cool nonchalance, when it occurred to me that most of these guys were maybe not great people. To which those watching with me said, in other words, duh. Baseball players were my childhood heroes, and while there are players like Sam Fuld respected for how they think about the game, I think most baseball fans, including myself, generally grow to favor a player for their athletic performance, or how they wear their hat.

The Mariners players are probably just fine human beings, don’t get me wrong, but are they kind of people that I could be friends with? How does Justin Smoak treat his wife? How does Dustin Ackley vote? What’s the deal with Cano’s child support issues? What do these guys, making at least the major league minimum half-million dollars, think about Seattle’s rise in minimum wage? Brandon Maurer looks like might be a fan of legalized marijuana…what does Jack Z’s drafted core of white dudes from Florida and the Carolinas think about gay marriage?

That said, it’s clearly unfair to judge baseball players on their beliefs. Carl Everett doesn’t believe in dinosaurs. So what?

The internet has been abuzz with the tragedy and ongoing protests in Ferguson for a while now, and this puts the St. Louis Cardinals in an tricky position. People have strong, vitriolic and polarized responses to the Ferguson protests, and the Cardinals clearly wanted to remain as neutral as possible without leaving the issue unaddressed. Here is the team’s official statement:

“Ladies and gentlemen, for over a century Cardinals baseball has been an integral part of the fabric of St. Louis — bringing us together as a community and enriching our lives in so many important ways. St. Louis is good community with good people who care about one another, our neighborhoods and our city. In recent days we have all been heartbroken by a series of violent events that do not reflect who we are as a people. We ask that you join us tonight in taking a stand against violence as we unite as one community.”

Here’s what Mike Matheny had to say:

“It’s a sad situation. It’s a tough situation for our city. Hopefully, all the voices that are trying to get this resolved get it resolved quick…This is a great city with a lot of great people and we’d just like to all see this get resolved.”

At face value these comments seem admirable, nonpolitical. That said both, Matheny and the Cardinals also seem to be wishing this all away. Perhaps when Matheny wants everything “resolved,” he is quietly suggesting police reforms. Perhaps when the Cardinals refer to “violence,” they refer to all of the original shooting, looting, and police response to protesting. But, as I read those statements, the team and Matheny just want things to get back to normal.

St. Louis prides itself on being the both the kind of city those statements describe, and a baseball town, averaging both the second-highest attendance and second-best TV ratings this year. Not only has the status quo has been great to the Cardinals organization and great for baseball fans, but also it would a big stretch to lay any blame on a baseball team for underlying racial issues in a given city.

But. That the Cards broke camp as one of three clubs with no African-American players is almost certainly not because of any malignant franchise philosophy rather than because MLB has seen a huge decline in African-American ballplayers. In the 1970s, baseball was 27% African-American, now it’s 8.5%. It should be noted that the Cardinals are known for drafting college players, and that may have an impact on the racial chemistry of their teams but the Cardinals draft the way they do for strategic reasons, and they’ve obviously been really successful.

What the Cardinals do demonstrate is the whiteness of baseball. Baseball has increasingly become a game of privilege. The decline in African-American players has in some part influenced by the expense of baseball equipment compared to other sports while income and wealth inequality has grown since the 1970s and the gap between African-Americans and Caucasians is wider than it’s ever been before. Many, including myself, treat baseball as an escape and an entertainment, and as an entertainment I don’t think baseball’s demographics demonstrates an issue itself so much as it demonstrates privilege, and white privilege, in general — it’s a privilege to be entertained.

Do our entertainers have to be good people? No. They have to be entertaining.

That said, many rappers have been vocal in their support of the Ferguson protesters (while others have not). That a rapper might be more articulate than a baseball player, or manager, shouldn’t be any surprise in that rappers make their living with language. I don’t expect Robinson Cano or Matt Adams to have a stance or statement about Ferguson, and it shouldn’t be expected of them.

The Cardinals, though, probably felt they had to make a statement, and they did. To attempt neutrality on a subject like Ferguson is tough, as it’s such a polarizing subject, and neutrality here is akin to apathy. What the Cardinals want is a move back to status quo, for financial reasons or otherwise, and as a baseball organization in a billion-dollar industry they shouldn’t be expected to want anything else.

Before Ferguson politicized the idea of St. Louis, the Cardinals were already busy making themselves look bad.

Mike Matheny, All-Star Game manager, started Adam Wainwright over Clayton Kershaw. He used two Cardinals relievers as well, so, in total, Cardinals pitchers had one-third of the innings in a loss that he probably thinks counts. Wainwright went on to admit to ‘grooving one’ to Derek Jeter and the Cardinals, in general, looked terrible.

After the All-Star Game, the Cardinals announcers played off Matheny’s move as rewarding his guys, and said it’s what All-Star managers usually do. In a more recent game they described Kolten Wong as the clear front-runner for NL Rookie of the Year, despite his having about one-third the WAR of Billy Hamilton at the time. Hamilton has a skillset easily appreciated by traditional measures, so while his UZR has certainly inflated his WAR, it’s also tough to look past a .270 batting average and 40+ stolen bases.

The Cardinals organization seems to like to toot their own horn. On the one hand, what team doesn’t? On the other hand, Matheny and the announcers both have demonstrated an inability to act with fairness and understanding when ‘their own guys’ are involved. So who are the Cardinals’ guys in Ferguson? They’d tell you it’s not any group or side, but the language of their statements suggests they’re certainly not with the protesters.

In 2001, in the midst of their 116-win season, the Mariners asked the city of Seattle to shut up the iconic trains whistles in broadcastable earshot of Safeco Field. Their reasoning, as offered to the city:

“[To] ensure that Seattle and Safeco Field are shown in the best possible light — something we are sure you will agree is important given the less than favorable opinion many people have of Seattle in the wake of the WTO and the Mardi Gras riots.”

The Mardi Gras riots mentioned were racially charged and resulted in 70 injuries and one death. Neither event is looked back on rosily. But in 2001 the Mariners, and in 2014 the Cardinals, missed the point.

Bill James defined sabermetrics “as the search for objective knowledge about baseball” and FanGraphs is an extension of that search. Matheny wanting Ferguson to be “resolved” is a little different than wanting a pitcher to resolve an issue in his mechanics. Objectively there is something wrong happening when people protest. Protesters feel there a problem or inequality, and whether they are justified may be subjective opinion but in this case someone died needlessly. Wanting that to go away isn’t going to fix anything. Objectively there are still a lot of things wrong with our country, and baseball isn’t one of them, but MLB shouldn’t position itself in the way of progress either. This is the sport that Jackie Robinson played, after all. Baseball can make a difference.

As you’ve probably heard by now, the Baltimore Orioles have made a habit of outperforming their run differential these last three years. In 2012, they finished the year 93-69, but their +7 run differential suggested they didn’t play much better than a .500 team. This year, they’re at it again. They currently sit atop the American League East with a 73-52 record, but their peripheral stats suggest they’ve lucked into a few wins along the way.

This has inevitably led to some disagreement over the true talent of recent Orioles teams. On the one hand, it’s been well established that things like BaseRuns and Pythagorean records do a pretty good job of predicting a team’s win-loss record. But at the same time, Buck Showalter’s Orioles have been pulling this off for a while now. Even if you understand and accept the concept of random variation, its a little hard to believe that the Orioles’ run has been entirely due to luck.

Jeff Sullivan recently penned a convincing article, dispelling the myth that clutch teams remain clutch over an extended period of time. He compared teams’ first-half clutch scores to their second-half scores, finding no correlation between the two, concluding that “team clutch” is not a repeatable skill.

Sullivan’s argument is pretty persuasive, but Major League teams today are sort of like like a Ship of Theseus: They experience lots of turnover over the course of a year, and come September, many look completely different than they did on opening day. Perhaps a comparison of half-seasons might not be picking up on the “magic” that often exists for only part of a year, when a team had the right combination of players on its roster.

To test whether this might be the case, I looked at month-to-month correlations for all consecutive months from 2009 to 2013. I also broke things up by hitting clutch and pitching clutch to see if there might be a phenomenon that exists on only one side of the ball.

There isn’t much going on here, as all three trend lines are pretty darn close to flat. But we do see a slight upward slope to the trend line for pitchers. Its not enough to be statistically significant (P-Value=.27), but maybe it could be picking up on something. For instance, it doesn’t seem far-fetched that some managers might be better than others at deploying relievers in situations where they’re likely to succeed. The 2012 Orioles’ bullpen, after all, was more clutch than average in all six months of the season. So maybe their success had something to do with the way Buck Showalter managed his bullpen? Let’s see if we see anything more definitive by breaking up the correlations up by starters and relievers.

Nada. Both rotation and pitching clutch show no signs of correlation, which implies that the hint of a relationship for month-to-month pitching clutch was purely statistical noise. Pretty much any way you slice it, there’s just no evidence suggesting that team clutch is in any way a repeatable skill, even over very short periods of time. Some teams — like the Orioles — do manage to string together consecutive months of clutch performance. But the overall lack of correlation between consecutive months shows that a team’s clutch performance is about as random as a coin flip. If you flip a coin enough times, you’ll eventually get 10 heads in a row. By that same logic, you’re bound to find a stretch as extreme as the Orioles’ if you string together enough three-year stretches.

All statistics courtesy of FanGraphs and their infinitely useful splits data.

I’ll try to avoid covering too much of the same ground covered right here on Wednesday, but talk about why the Yoenis Cespedes trade will still probably help Oakland this season.  The A’s are generally considered a pretty smart front office, and I think they saw a problem that needed fixing.  I also think that their offense is worse without Cespedes, so we will have to get to that too.

The main source of confusion in this trade stemmed from the fact that the pitching staff seemed to be a strength.  So why would a team trade away one of their middle of the order bats to bolster an already solid part of their team?  The answer is that the team wants to win in the playoffs, and the horses of the rotation for the first half were not going to continue their success.

Jesse Chavez had posted a 3.14 ERA prior to the All-Star break, and since then it has been 4.37 with most of that has been from the bullpen.  Cracks in his performance were showing in June and he was failing to get deep into games, so there was no way they were going to count on him as an option in the postseason.

Drew Pomeranz was showing some signs of being an option before he got hurt, but the injury cut short his opportunity and made him too big of a question mark to count on.

The most important guy in the equation was Sonny Gray.  He has been very good so far this year, but he is heading into uncharted territory fast and it is starting to show.  Last year Gray threw 182.3 innings between triple-A and the majors.  He is now at 162.7 with more than a month before the playoffs even start.  He was still going strong in July, but his velocity had peaked in late May and early June and has slowly been coming down ever since.  They were right not to trust him if August is any indication.  Since the trade Gray has posted a 4.94 ERA, his K-rate is down, and players are hitting him harder.

That all leaves Scott Kazmir and two players that had already been acquired in Jeff Samardzija and Jason Hammel.  Hammel has been bad since the trade with only one start where he made it 6 innings.  Honestly Samardzija’s been pretty bad as well, but prior to the Cespedes trade he had put together a couple good and a couple mediocre starts.  If your only two guys you trust going into October are Samardzija and Kazmir, things are probably not feeling very good.

All of this lead to Jon Lester who so far has been everything they want him to be except that the team has struggled during the time since his arrival.  The hitting collapsed with Coco Crisp, Jed Lowrie, Brandon Moss, and Derek Norris being especially bad.  Steven Vogt also came back to Earth a bit, and the Jonny Gomes/Sam Fuld replacement for Cespedes has under-performed so far.

The solid 3-4-5 of Cespedes, Josh Donaldson, and Brandon Moss lost a piece, and they don’t really have a great option to plug into the 5 hole consistently.  Josh Reddick has come on recently to help in a somewhat depleted offense, but they are keeping him at the bottom of the order since he has been anything but trustworthy over the past couple seasons.

This has hurt the offense for sure and simple confidence intervals of before and after the trade show a significant drop in output.  At the same time I assume they saw this coming to some extent.  Guys like Norris and Vogt were playing way over their heads and were likely to regress some.  Only the weird collapse of half the offense at one time has made it look as bad as it is.  It is unlikely that this rough stretch will be sustained.  It also didn’t help that the Royals, Rays, Braves, and Mets were all on the schedule and are above-average run-prevention teams.

If I were the A’s I would still be happy about this trade.  Lester, Samardzija, and Kazmir is a much better way to head into the post season.  Catching the Angels just became more likely due to the unfortunate loss of Garrett Richards too.  Billy Beane has been to the playoffs, and almost certainly will be again this season.  He wants to win in the playoffs, and this pitching staff gives him a good opportunity to do so.

I was listening to the Jonah Keri Podcast on Grantland recently, and he had Phillies beat writer Matt Gelb on the show. Gelb talked about all the sad things that Phillies fans are already tired of discussing, but he did make a statement that I found particularly poignant. He described Ben Revere’s season as something to the effect of “the emptiest batting average ever.” By empty, he means that while Revere is hitting above .300, an impressive feat in this offense-starved MLB landscape, he does so with almost no walks or extra-base hits. His value at the plate is almost entirely in the form of singles. This comment got me thinking: just how empty is his batting average?

As of this writing, Revere is hitting .314 with a .331 on-base percentage and a .371 slugging percentage. For comparison, the average player has a substantially worse batting average (think .240) but with a similar OBP and a substantially better SLG. To illustrate with normal stats, Revere has 27 total doubles, triples, homers, and walks this year. So far in 2014, there are 42 players with at least 27 doubles, 8 players with at least 27 homers, and 144 players with at least 27 walks.

But how rare is it to have this single-happy nature with such a high average? To look for players to compare to Revere historically, I looked for other player seasons since 2000 which had enough plate appearances to qualify for the batting title with a batting average at least as high as Revere’s but a walk rate and isolated slugging (slugging minus batting average) below his.

But there weren’t any, so I extended the search back to 1980.

Still nobody. 1960?

Nothing.

1900?

Zilch.

Now, to be fair, Ben Revere himself hasn’t completed a full season, so let’s use a more relaxed criterion of 400 plate appearances (Revere has 459).

OK, you get it.

In fact, since 1900 (it’s not worth going earlier because seasons were much shorter then), the only player with at least 400 plate appearances that had as high of a batting average with as little other hitting value is … Ben Revere. That’s it.

I’m not really sure that there’s much to be done with this information, but it’s a pretty shocking finding. As a member of a roster that’s overpaid and underperforming across the board, Revere’s limited skillset has been overshadowed by his more compensated counterparts. However, I was fascinated to discover that on a team that has had plenty of notable failings, Revere has had perhaps the most “unique” and “special” stats of any of them, as long as you’re not taking annual salary into account.

If you disregard his sub-par defense (especially compared to what you would expect from a guy with his speed), Revere really isn’t a terrible offensive player. If you took away all of his steals and instead turned that many singles into doubles, he’d have a slugging percentage around the league average. The problem is, a single followed by a steal isn’t as valuable as a double because it doesn’t advance runners on base, so his value would really be something less than that of a player with league-average slugging. Even if he posts a batting average way above the mean in any given season, he never walks or gets extra-base hits, so he has to sustain that mark against all kinds of luck and defensive factors in order to give the Phillies even passable offensive value. It’s a game that the Phillies seem interested in playing, and it’s defensible because of his obviously high average and stolen base totals, but I’m just not sure if they’re going to win that way.

Take a turn around Twitter or any major baseball news source and you’ll hear a familiar echo about the former best team in baseball; the offense hasn’t been the same since the deadline.  When the A’s traded away Yoenis Cespedes for Jon Lester, the impact to the lineup was noticeable.  They wagered they could get the same level of production out of some combination of Jonny Gomes, Stephen Vogt, and Sam Fuld.  In the first half of the season, the A’s were a top-six team in wOBA, OBP,and wRC+ all while being second to last in BABIP.  It’s safe to say they were rolling. Now they aren’t.  Since the deadline, the A’s have become a bottom-third team in all the aforementioned stats.  It’s easy to look at these stats and say that Cespedes was clearly the catalyst of something in the offense.

While much has been written about the rumors of Oakland emphasizing clubhouse chemistry the last couple years, Cespedes has never really been written as one of the chief leaders in that category.  We typically hear names like Coco Crisp, Scott Sizemore, the aforementioned Jonny Gomes, and Sean Doolittle mentioned there.  Cespedes by all accounts was just a crazy athletic guy who didn’t really cause any trouble, but wasn’t exactly a team leader.  Yet the fact remains: the A’s have refused to hit since the deadline. Sure, 17 games isn’t a gigantic sample size, but it’s pretty reasonable when evaluating team performance.  Baseball Prospectus just three years ago theorized that a reasonable prediction could be made of a team’s overall season after fifteen games,  so we’ve got something substantial to work with.  Is there another pattern, though?  Let’s take a look at the team’s month by month performance.

We see a steady decline here in the A’s performance, not a sudden jump.  The A’s started off really hot, leading the league in most offensive categories in April.  A notable decline can even be seen in May, as the A’s began their meteoric rise to the top, though they held steady in the top three in most categories.  In June, the team dipped even further, down to a mark that was only slightly above average.  They looked to be leveling off there to a rather league-average team in July, which wasn’t encouraging, but maybe suggested a possible rise back up to looking like a playoff team. In August, though, the wheels have come off.  The A’s have dipped below league average in most categories, and their win totals have suffered as well.  Can we blame all of this on Cespedes?  Let’s take a look at some wOBA numbers for chief contributors to the Oakland offense:

It’s a bit cluttered, but the dark blue line in the middle labeled wOBA is the team as a whole; see the steady decline as we’ve noted.  In April, we see all of these guys hovering between a .300 wOBA and somewhere above .420.  Nearly all of them are now either .300 or far below it; the one exception being Josh Donaldson, who has picked it up again since a dismal June.  Even Cespedes, having been traded to the Red Sox, is having an unremarkable August since performing poorly in July.  Let’s take a look at a wRC+ graph, with some of the members removed for clarity:

Here we see six players who routinely batted in the top five in the batting order having horrible Augusts.  Stephen Vogt and Brandon Moss, two lefty platoon bats being pressed into full-time duty in the outfield lately, lead this group with a 91 wRC+, which is below the average line.  John Jaso, Coco Crisp, and Derek Norris have been downright horrible, with wRC+’s in the barely digestible territory. So yes, the A’s have been bad since Cespedes has left the team.  It’s clearly not just the loss of his bat; the vast majority of the team, outside of Josh Donaldson and the surprisingly resurgent Eric Sogard and Josh Reddick, have been really, really bad.

So if the whole team is flailing, perhaps Cespedes was more of a sparkplug than we previously had attributed?  More importantly, did Billy Beane fail to see a trend here?  The A’s were trending downwards in hitting as demonstrated, so why the need for pitching?  Well, the A’s were unfortunately not exactly trending very well in pitching either.  They were third in pitcher WAR through April, but then plummeted to 19th in May, and further dipped to 21st in June before rising a bit to 17th in July. The A’s were a decidedly middle of the road team when it came to pitcher WAR, and FIP seems to agree, ranking them about the same spot everywhere.

So why make the trade?  If anything, this trade has only served to confuse fans.  What do we make of a team with three above-average catchers who all tank right after a trade for a top-flight starting pitcher?  While all the fans are clamoring for Jimmy Rollins to come and help the middle infield, we’ve got Eric Sogard being one of the few bright spots in the offense, and nobody seems to care. All we know is that the A’s are in trouble.  Whether it’s because Cespedes was the glue or because the A’s are peaking at the wrong time, they’re all of a sudden facing down the dire straits of a one-game coin flip at the end of the season, despite being the most aggressive pursuer at the trade deadline. The A’s can cling to a few bastions of hope; maybe their BABIP dropping all the way to .260 in August shows that they’re just a bit unlucky.  It’s either that or face the fact that sometimes the best-laid plans of mice and men fail, and pray that Jason Hammel doesn’t have to start the Wild Card game.