To express the extent of a player’s confidence is difficult, and using numbers to back up this assertion is even harder. When a player lacks confidence, it can be seen through a slew of on-field mannerisms that don’t always present themselves inside statistics. Instead, the numbers tell us the story of a pitcher, once of dominant form, who is struggling to get outs and display any sort of consistent performance. The statistics paint this picture about Matt Harvey. They tell us a tale of dominance, hindered and erased by injury and ineffectiveness. Although this story is told, it seems to be far from the truth. I believe in an alternate story. A story that displays a human being struggling with the confidence to throw his pitches and retire hitters. A lack of confidence stemming from a large set of off-field hindrances and a set of recent on-field struggles. A problem that will be moved past and put behind in the months to come, making it only a distant memory to both Matt and Met fans.

If we rewind back to September of 2015, we can see that Harvey is no stranger to hardships or headlines. After Tommy John surgery following his stellar 2013 campaign, he seemed back to form throughout 2015, culminating in an impeccable playoff start against the Cubs and a World Series game 1 nod. Throughout the season, questions about Harvey’s innings limit hovered around the Mets clubhouse, reaching its climax in early September. After a start against the Philadelphia Phillies where Harvey exited early due to dehydration, agent Scott Boras spoke about the doctor’s indication that Matt should not exceed 180 innings pitched that season. With Matt already at 166 1/3 innings, it seemed like the Mets organization was directly ignoring these suggestions.

This back and forth between the front office and Boras propelled Matt into the spotlight preceding his next start against Washington, who had become their rival in the midst of a pennant race. He pitched poorly, to the tune of 7 R (4 ER) in only 5 1/3 innings. This tough outing doesn’t hold a torch to his current struggles, but the difference in approach between this start and his recent starts form an interesting comparison.

Throughout this start in particular, and the entirety of the 2015 season, Matt Harvey was unafraid to throw his fastball to any hitter. He challenged hitters like Bryce Harper, in the midst of an MVP season, with fastball after fastball. In Harvey’s most recent start, he wouldn’t even challenge Manuel Margot with the same. Of his 74 pitches in that 2015 start, he threw 51 fastballs 95 and above, constantly pounding the zone. In his most recent start, Harvey nibbled around corners, he never challenged hitters, and he relied on his breaking ball (usually out of the zone) even when behind in the count. This tendency showed a lack of confidence to throw his fastball and challenge hitters, something that Harvey needs desperately to be successful. Overall, the dichotomy in approach between 2015 and 2017 for Matt is striking. Here are some of the numbers based on his position in the count:

2015, 2017
AHEAD
CU, CH: 22.3%, 19.7%
SL: 15.5%, 23.4%
FA, FT: 62.2%, 56.9%
BEHIND
CU, CH: 19.0%, 25.3%
SL: 16.5%, 21.7%
FA, FT: 64.6%, 53.0%
TOTALS
AHEAD% 32.6%, 20.8%
BEHIND% 20.8%, 25.9%

In 2015, when ahead in the count, Matt threw 62.2% fastballs. When behind, he threw even more, to the tune of 64.6% of the time. Because of his ability to pound the zone with his fastball, he spent 32.6% of his time ahead in the count while only 20.7% behind. This allowed him to control the pace of the at-bat and the expectations of the hitter. When he wanted to break off a curveball or a slider it became much more effective in relationship to his established fastball.

So far in 2017, he’s been unable to get ahead in the count or develop any rhythm with the pitch. His inability to challenge hitters has left him nibbling around the plate, leaving him ahead in the count only 20.7% of the time. This problem grows when behind in the count, as Harvey continues to throw off-speed pitches 47% of the time. His inability to command these pitches leads to even worse counts, and compounds the problem. Throughout his most recent start again San Diego, Harvey continued to nibble around the corners of the zone, seemingly afraid to challenge hitters with his fastball or throw off-speed pitches consistently in the zone.

This tendency, pointed out by Ron Darling during the SNY Broadcast, can be evidenced by his complete change in pitch usage as shown above. Although diminishing fastball usage is occurring league round, Harvey has to use his fastball more consistently to be more effective this season. By establishing his fastball early, he can play off of it, creating more effective offspeed pitches as well as more powerful fastballs. To be a Cy Young caliber pitcher, you have to trust your stuff and believe in your ability to dominate. As of now, Matt doesn’t believe in either.

2017 has been full of surprises so far. The Cubs were supposed to run away with the NL Central, but are struggling to stay above .500. The Backstreet Boys were supposed to drop an album sometime this year, but it’s May and we’ve heard squat from Nick Carter. And most intriguingly, this was the year we were supposed to see a radical change in who batted lead-off — but not much has changed.

Journalists were forecasting 2017 as the year of the slugging-lead-off hitter. Zach Kram of The Ringer boldly proclaimed “The Batting Order Revolution Will Be Televised” in explaining how more and more managers are batting sluggers, bonafide power bats like Kyle Schwarber and Carlos Santana, lead-off. This season seemed poised to be the year that we saw managers reaping the benefits of giving their best hitters more at-bats.

The folks over at The Ringer weren’t the only ones — 538, Fox Sports, and ESPN have all described the coming revolution. But there’s one small problem — the revolution isn’t having that big of an impact so far.

Okay, sure — lead-off hitters have, technically, hit for more power than they have in years past. League-wide, we have seen ISO for lead-off hitters in the past few years jump up faster than Bartolo Colon when he hears the words “unlimited buffet.”

What could be to blame for such a power surge from the leadoff spot? Hint: it has a lot less to do with the fact that managers are batting their sluggers in the lead-off position than you’d think.

Remember the league-wide power surge that the MLB encountered last season? Power across the league skyrocketed — curiously enough, in the exact same manner in which lead-off hitters’ power skyrocketed.

While there are some variations from 2002-2013, the recent power spike from lead-off hitters is almost entirely explained by the league-wide power spike. In fact, if we look at lead-off hitters’ ISO relative to league ISO, we find that lead-off hitters are hitting for less power than they did in 2016.

This is not to say that there has been no power surge among lead-off hitters — as you can see above, adjusted ISO in the lead-off spot has risen steadily since 2012. Perhaps that is the result of batting sluggers as lead-off hitters. But the leaps and bounds in production from the lead-off spot as predicted above simply haven’t come to fruition. These lead-off hitters are power-surge imposters! It looks like they’re maintaining the same power from last year, when relative to the league, they’ve actually lost power.

The narrative of the power-hitting lead-off batter taking the MLB by storm seems legitimate on the surface, in no small part thanks to Michael Conforto‘s renaissance as a top-five hitter while starting off games, or Charlie Blackmon’s position atop the RBI leaderboards despite spending his season in the lead-off spot — and indeed, these players are providing additional value by leading off.

But these are only individual cases. The “lead-off hitter revolution” isn’t having as much of an impact league-wide as the revolutionaries might like to think — after all, Dee Gordon and Billy Hamilton still occupy lead-off slots with their .066 and .084 ISOs respectively, nevermind the fact that the poster-child of the revolution, Schwarber, is making up for the sophomore slump that he missed by being injured for all of 2016.

Lead-off hitters are technically hitting for more power, but so is everyone else. Blaming the huge spike in power in the lead-off spot on managers batting hitters lead-off is to ignore significant league-wide trends, and miss the big picture. Maybe there is a small impact caused by the new lead-off philosophy, but it certainly is not bringing unheralded power and production to the lead-off slot. The revolution might not be a bust (yet), but it still has long ways to go in order to make an impact.

This is for now the final article of my launch angle series (Sorry Carson, or whoever edits all those articles).

Alan Nathan wrote an article that suggested that a steeper attack angle (upward swing angle of the bat) produces more extra-base hits but has a cost in power.

That makes sense since the average pitch only has a downward angle of like 5-10 degrees and if you swing up at 20 degrees you are on plane with the pitch for a shorter time.

Unfortunately, we don’t have attack angles for pro players in games, because there are cheap bat sensors that measure that now but they have only been used in ST and futures games (suggesting attack angles of like 8-15 in most cases I have seen), but I will assume that the average exit angle over a long sample should be pretty similar to the attack angle, or at least correlate closely.

For that, like in my last post, I looked at guys that had at least 500 ABs in 2015-2016.

I looked at LAs of <7, 7-9, 9-11, 11-13, 13-15, 15-17 and >17 degrees.

I did not really find very big differences. Below 9 degrees it was about 2 percent lower than at the higher angles, but after that there isn’t a big change. Even looking at the small sample above 19 degrees, it was only elevated to 21.6%, which is higher but not spectacular (and it was a small sample of only seven batters).

To look further I looked at exit velo. If I looked at the batters above 91 mph they averaged 23% Ks, vs 19.1% for the below-91 group.

So there may be some penalty for swinging hard, but there also might be a selection bias, since low-power swing-and-miss guys are weeded out while power hitters with bad contact skills produce more and stay in the league longer.

Overall, looking at those data, I would say that contact is mostly a skill that is separate from launch angle. In my prior articles I have shown that there is a punishment for  angles that are too high, but it seems to come more in the form of pop-ups and routine fly outs and thus lower BABIP, and not in the form of whiffs. Now we know there are some high-LA, high-whiff guys like Chris Davis for example, and those guys do trade BABIP for ISO with higher LAs to get the most out of their contact, but the more extreme uppercut likely isn’t the source of their whiffs but an attempt to compensate for them by trying to strengthen their strength while “punting” their weakness.

I looked at the Statcast leaderboard from 2015 to early 2017 and sorted for below-average (88 MPH) and above-average exit velo for batters who had at least 500 ABs in these two and something years as an arbitrary cutoff.

The Top 15 in wOBA above with an EV of above 88 MPH averaged a wOBA of .402 while the top 15 below 88 MPH averaged .351. As expected, the higher EV Group has a higher wOBA than the lower group.

The average LA for the harder-hitting group was 14.16 +- 2.5 while the LA for the softer-hitting group was 11.75+- 4.3. It seems like the softer group does better at a lower LA and there also is a greater variance for different LAs.

I also looked at the worst hitters of each group. The bottom 20 in the soft-hitting group came in at 10.15 degrees +- 4.2.

In the hard-hitting group, the worst wOBA hitters averaged 11.15 degrees +- 3.8.

So there seems to be some relationship of LA in the harder-hitting group, while it doesn’t matter much for the below-average group.

Now, if we expand it to 90+ MPH you get an average angle of 13.43 +- 2.9 for the good wOBA group and 12.26 +- 3.7 for the lower level group.

So the conclusion seems to be that harder hitters benefit more from increasing the LA while for the soft hitters it doesn’t seem to make a difference. Of course, I did not factor in Ks and BB in my calculations (I unfortunately had no access to wOBA/con in the leaderboard) and that is probably a big influence.

Overall, when I did a correlation test of wOBA and LA, I didn’t really find anything significant for both groups.

Where it got interesting was when we got into more extreme launch angles. The top 15 wOBA below 9 degrees was .328 (-23 points compared to the best soft hitters) while for the harder hitters the average was .358 (-44 compared to the best hard hitters).

At 7 degrees, the performance of the hard hitters was .340 (- 62 and the first time it was worse than the best soft hitters) while for the best soft hitters below 7 degrees the average was .320 (-27) and only marginally worse than minus 9.

Looking at the other end, the top hitters above 15 degrees had a wOBA of .376 (-26 compared to all LA) — maybe this is due to sacrificing contact for more lift?

And finally, in the softer-hitting group, there were only 12 guys with a LA above 15 degrees, and their wOBA was .314 (-37).

Overall, it seems like LA only has an effect if you get farther away from the average (around 11-12 degrees). Harder hitters can benefit from going higher, while for soft hitters it doesn’t matter much, as long as they stay somewhere near the vicinity of average.

The guys who really benefit from a LA change are the really hard hitters with really low angles.

In the modern MLB, the record for the longest losing streak sits at 23 games, set by the 1961 Philadelphia Phillies, while the longest winning streak sits at 21 games, set by the 1935 Chicago Cubs.  In recent memory, the 2002 Oakland Athletics come to mind, with their Moneyball-spurred 20-gamer, taking them from 68-51 to 88-51 and first in their division.  Winning streaks captivate a fan base, and attract league-wide attention, but little is understood about their nature.  How much luck is involved?  Are certain teams or players more inclined to be streaky?  Are teams really more likely to win their next game if they’ve already won a few in a row?  In this piece, I’ll outline a simple model for what legitimate team-level streakiness might look like, and see if any interesting behaviour arises.  I was able to do this after reading the section on Markov Chains in Linear Algebra by Friedberg, Insel and Spence.

The Model

This model only requires two inputs: the probability of a team winning a game given that they won the previous game (hereafter P(W|W)), and the probability of a team losing a game given that they lost the previous game (hereafter P(L|L)).  Admittedly, this assumes ballplayers have very short memories, but.  The first thing we need to generate is what’s called a transition matrix:

The first row contains the probabilities that a team will win a game based on what happened in the previous game, and the second row contains the probabilities of losing.  Notice that the entries of each column sum to 1, so we can rewrite this as

Without going into too much detail, all we need to do is multiply matrix A with itself a lot, and find the limit as we do this infinitely many times.  This will give us another matrix which will contain two identical columns, each of which will correspond to the long-term probabilities of winning given a team’s P(W|W) and P(L|L) values.

For example, if our team has P(W|W) = 0.6 and P(L|L) = 0.5, we’ll have
,
and the limit of A^m as m goes to infinity is
.
So our long-term probability of winning will be around 0.56.  Over the course of a full season, then, this team would expect to win around 90 games.

Now we can examine various cases.  It may not be surprising to find that if we have P(W|W) + P(W|L)  = 1, we’ll have a long-term probability P(W) = P(L) = 0.5.  That is, no matter how streaky a team is, if their probabilities of winning after a win and after a loss sum to 1, their expected win total over a 162-game season is 81.  But what if we look at a given long-term probability P(W), and see what conditional probabilities P(W|W) and P(L|L) give us P(W)?  In the table below, pay special attention to the boxes with P(W) values of 0.5, 0.667 (our incredible team) and 0.333 (our really really bad team).

(Pardon the gaps in the table — my code had a bug that made it output zeros for those parameters, and I didn’t feel like the specific numbers were integral to this article so I didn’t calculate them manually.)

For P(W) = 0.5, we notice a straight line down the diagonal – which makes sense, given that we know P(W|W) + P(W|L) = 1 for these entries.  For P(W) = 0.667 and P(W) = 0.333, we have the following pairs of P(W|W) and P(L|L):

P(W) = 0.667 — (P(W|W), P(L|L)) = (0.5, 0) or (0.6, 0.2) or (0.7, 0.4) or (0.8, 0.6) or (0.9, 0.8)

P(W) = 0.333 — (P(W|W), P(L|L)) = (0, 0.5) or (0.2, 0.6) or (0.4, 0.7) or (0.6, 0.8) or (0.8, 0.9)

So our two-thirds winning team could just never lose two games in a row and play at a .500 clip in games following a win.  Or they could lose a full 80% of their games after a loss, but be just a little bit better at 90% in games after they win!  How could a team that never loses two games in a row be the same as a team that is so prone to prolonged losing streaks?  It’s because we selected this team for its high winning percentage, so even though P(W|W) and P(W|L) actually sum to less in this case (1.1 instead of 1.5), the fact that this team wins more games than it loses means it’ll have more opportunities to go on winning streaks than losing streaks.

Likewise, our losing team could never win two games in a row but play at .500 in games following a loss, or they could be the streaky team who wins 80% of games following a win but loses 90% of games following a loss.

These scenarios are illustrated below.  The cyan dots correspond to the following pairs of points (P(L|L), P(W)) from top left in a clockwise direction: (0,0.667), (0.8, 0.667), (0.9, 0.333), (0.5, 0.333).  These are exactly the scenarios discussed above.

These observations indicate a more general property, which will sound trivial once we put it in everyday baseball terms.  If your long-term P(W) is above 0.5, and you have to choose between two ways of improving your club – you can improve your performance after wins, or you can improve your performance after losses – you should choose to improve your performance after wins.  And if your long-term P(W) is below 0.5, you should choose to improve your performance after losses (up until you become an above-average team through your improvements, of course).  In other words, if you expect to win 90 games (and hence lose 72), you want to improve your performance in the 89 or 90 games following your wins rather than in the 71 or 72 games following losses.

Conclusions, Future Steps

I don’t have anything groundbreaking to say about this experiment.  It’s obviously an extremely simplified model of what real streakiness would look like – in the real world, the talent of your starting pitcher matters, your performance in more than just the immediately preceding game matters, as well as numerous other factors that I didn’t account for.  However, I feel comfortable making one tentative conclusion: that the importance of the ace of a playoff contender being a “streak stopper” (i.e. one who can stop losing streaks) may be overstated, simply because the marginal benefit from such a trait is smaller than the marginal benefit from being a “streak continuer.”  I have never heard of an ace referred to as a “streak continuer,” even though this model indicates that on a good team, this is more beneficial than being a “streak stopper”.

I don’t think it’s worth examining historical win-loss data to compare with this model, as this was not intended to be an accurate representation of what actually happens; rather more of a fun mathematical exploration of Markov chains applied to baseball.

Thank you for reading!  Questions, comments, and criticisms are welcome.

Devon Travis is having a rough start to the 2017 season. As I’m writing this, he has “hit” .148/.207/.222, good for a wRC+ of 16 and WAR of -0.5. Fans are openly wondering if he should be sent back to triple-A. But all is not lost! If you look past the surface stats, there is hope for the young Blue Jay. Let’s explore.
Read the rest of this entry »

Out of the many differentiae that make up José Carlos Altuve’s thumbprint on baseball, from his 5’ 6” stature, to getting cut from his first tryout with the Astros but showing up the next day anyway, his groundball percentage would likely not rank toward the top for most fans of the Venezuelan. However, most of said fans have not seen this graph.

As you can see, after decreasing his rate consistently for four seasons, Altuve is hitting more groundballs and hitting more balls toward right field in 2017 than at any other point in his career. Although this is in a sample of just 88 plate appearances, and may be a statistical blip, I think that with a hitter like Altuve it is worth investigating.

Altuve’s BABIP is also a high, even for him, .393. Apart from being the most satisfying stat to say out loud, BABIP is also the one thing that alters early-season statistics more than any other, but .393 for Altuve isn’t like the clearly unsustainable .455 that Steven Souza Jr. is currently running. It’s just .044 points higher than his last season rate, so while it’s probably not sustainable (DJ LeMahieu led the league in BABIP last year at .388) it’s not lifting him to his 134 wRC+ by itself.

So what’s the reason for this? Is it a change in approach? A reaction to what other teams are doing? Teams don’t appear to be shifting Altuve, so it doesn’t look like he’s trying to beat them by hitting grounders through an open hole. It does, however, look like maybe teams are attacking him down and away slightly more than in the past. Here are Altuve’s heat maps the last two seasons.

2016

2017

There does appear to be a slight uptick in balls in the bottom corner of the zone, but it’s hard to call it a novel trend when the basic strategy against Altuve since he came into the league remains basically the same: down and away.

But the story doesn’t end there! There are two things that do appear to be starting a trend.

First, his Zone% (meaning the percent of the time that opposing pitchers throw him a strike) has been consistently trending downward since he came into the league. It’s currently at 46% according to Trackman, which is the lowest mark of his career.

Second, his Fastball%, according to Baseball Info Solutions, is at 49.7%, which is also a career low, and actually over 8 percentage points lower than the rate he’s seen in his career.

Now, this all makes intuitive sense. Altuve’s power has risen in recent seasons, and it looks like pitchers have adjusted accordingly; no surprise there. What I thinks is worthwhile in all this is that Altuve is adjusting right back. Before this past weekend in Tampa Bay, Altuve had no home runs on the year. He’s been going with what pitchers have been throwing him. They want to throw him offspeed down and away, and he’s been going with it, exchanging some of the power he took last year to keep his overall offensive profile as one of the league’s elite hitters.

Introduction

One of the most-discussed issues in Major League Baseball is the consistency of the strike zone. The rule-book strike zone states “The STRIKE ZONE is that area over home plate the upper limit of which is a horizontal line at the midpoint between the top of the shoulders and the top of the uniform pants, and the lower level is a line at the hollow beneath the kneecap. The Strike Zone shall be determined from the batter’s stance as the batter is prepared to swing at a pitched ball.” After watching games throughout the regular season and playoffs, it is easy to realize this is not the strike zone that is called. Each umpire has tendencies and dictates his own strike zone and how he will call a game. With the rise of PITCHf/x and Trackman in the last few years, umpires have been increasingly monitored and judged for their accuracy and impartiality. For this reason, umpires are criticized for incorrect calls more than ever before and I believe are now trending towards enforcing the rule-book strike zone more than in years past.

The purpose of this research will be to do two things. First, I will focus on identifying overarching themes where I look at finding how umpires are adjusting to modern technology but also how the rule-book strike zone is not the strike zone we know. After this, I will dive into a few umpire-specific tendencies. The latter would be helpful to teams in preparing their advance reports by knowing how certain umpires call “their” strike zone dictated by situations in a game.

Analysis

Using PITCHf/x downloaded through Baseball Savant, I have looked at major-league umpires since 2012 in regards to their accuracy in correctly labeling pitches, primarily strikes, and their tendencies dictated by specific situations. While the height of the strike zone is often influenced by the height of the batter, there are other factors to take into account such as the how the batter readies himself to swing at a pitch. Unfortunately, the information publicly available to conduct this research does not include the batter handedness, pitcher name, or measurements of individual strike-zone limits. For this reason, a stagnant strike zone serves our needs best. The height of the strike zone shall be known as 1.5 feet from the ground to 3.6 feet from the ground. This is the given strike zone of a batter while using the pitchRx package through RStudio when individual batter height is not included.

All PITCHf/x data is from the Catcher/Umpire perspective, having negative horizontal location to the left and positive to the right. The width of home plate is 17 inches, 8.5 inches to both sides where the middle of the plate represents 0 inches. After calculating the average diameter of a baseball at 2.91 inches, we add this to the width of the plate. Therefore our strike-zone width will be 17 + 5.82, or 22.82 inches. The limits we will then set are going to be -.951 to .951 feet (or 11.41/12 inches). Throughout the paper I will be referring to pitches that fall within the boundaries of our zone as “Actual Strikes” and pitches correctly identified as strikes within this zone as “Correctly Called Strikes.”

Called Strike Accuracy By Year

As Table 1 shows, correctly identifying strikes that fall in the parameters of the rule-book strike zone has risen substantially. While 2015 has a higher percentage of correctly called strikes, 2016 PITCHf/x data from Baseball Savant was incomplete, with 28 days’ worth of games unavailable at the time of this research. A rise of 5.90 percent correctly called strikes from 2012 to 2015 shows the rule-book strike zone is being more strictly enforced.

While this provides some information, we can also look into where strikes are correctly being called using binned zones. Understanding that the evolution of umpires over the last five years is taking place and trending toward correctly identifying strikes more today than in years past, we can analyze where, in the strike zone, strikes have been correctly labeled.

Called Strike Accuracy by Pitch Location

In Table 2, we can see a tendency among umpires. Strikes are called strikes more routinely over the middle of the plate and to the left (from umpire perspective). As I have mentioned before, the publicly available PITCHf/x data I used did not include batter handedness and I am unable to determine who is receiving the benefit or disadvantage of these calls. Presumably from previous research on the subject, lefties are having the away strike called more than their right-handed counterparts, explaining the separation between correctly identifying strikes in zones 11 and 13 versus 12 and 14.

Binned Strike Zone

While one may argue that there should not be strikes in these bordering zones, we consider any pitch that crosses any portion of the plate a strike. Due to our zone including the diameter of the baseball on both sides of the plate, the outer portion of the plate includes pitches where the majority of the ball is located in one of these zones.

Called Strike Accuracy by Individual Umpire

When gauging an umpire’s ability to correctly identify a rule-book strike, an 85.67% success rate sets the mark with Bill Miller, while Tim Tschida ranks at the bottom of this list, only calling 71.57% correctly. We can infer from Tables Three and Four along with Table One, that while umpires are calling strikes within the strike zone more often, they are still missing over 17% of these pitches. It is important to note that this information does not take into account incorrectly identifying pitches outside the rule-book strike zone as strikes, which when considering an umpire’s overall accuracy, should absolutely be taken into account.

Called Strike and Ball Accuracy by Count

One of the most influential factors in whether a taken pitch is called a strike or a ball is the count of the at-bat. We have all seen pitches in a 3-0 count substantially off of the plate called a strike, just as we have seen 0-2 pitches over the plate ruled balls. Table Five shows the correct percentage of strikes and balls by pitch count. While this shows that umpires are overwhelmingly more accurate at identifying strikes as strikes in a 3-0 count (91.06%) as compared to an 0-2 count (56.66%), we must acknowledge this only paints part of the picture. Umpires are conversely most likely to correctly labels balls in 0-2 (98.73%) counts and misidentify balls in 3-0 (90.32%) counts. I included their accuracy of correctly identifying both strikes and balls here as opposed to throughout the entire paper because we can clearly tell through this information that umpires are giving hitters the benefit of the doubt over pitchers. Umpires are far more likely overall to correctly identify a ball than a strike, as evidenced by the fact that there are no counts during which umpires correctly call less than 90% of balls.

The data in Table Five is corroborated by the visualizations in Figure One and Figure Two. These visualizations of the strike zone include pitches off of the plate and we can see that in a 3-0 count, a more substantial portion of the rule-book strike zone is called strikes while also incorrectly identifying balls as strikes. While in a 0-2 count, a smaller shaded area of the rule-book strike zone works with our findings that less strikes are identified correctly but more balls are correctly called.

Called Strike Accuracy by Pitch Type

The next area I looked at was whether pitch type significantly altered the accuracy of umpires. In order to do this, I grouped all variations of fastballs into “Fastball” and all other pitches into “Offspeed”, while omitting pitch outs and intentional balls. I was able to see how umpires fared in correctly identifying strikes by pitch type in Table Six.

Not surprisingly, we see Bill Miller near the top of the list with both Offspeed and Fastball accuracy. For umpires as a whole, the difference in accuracy between the two is not large (79.05% Offspeed accuracy vs. 78.91% Fastball strike accuracy). On the other hand, what may come as a surprise is the fact that eight of the top ten highest accuracies were for Offspeed pitches.

Called Strike Accuracy for Home and Away

One of the most-mentioned tendencies of referees or umpires in any sport is home-team favoritism. Whether a foul or no-foul call in basketball, in or out-of-bounds call in football, or a strike or ball ruling in baseball, many think that the home team receives more of an advantage than their visiting counterparts. Looking at top and bottom half of innings, away and home team respectively, we can identify trends and favoritism in major-league umpire strike zones.

While a difference of .62% accuracy may seem like a lot, especially in a sample size of over 650,000 total pitches, we can look at this on a game-by-game level to see the actual discrepancies. For simplicity’s sake, we can assume 162 games a season, making for roughly 11780 games played in our data set (this subtracts all games from the unavailable 2016 data). This leaves us with 23.03 Correctly Called Strikes out of 29.05 Actual Strikes for away teams per game, meaning that 6.02 strikes were not called. As for home teams, we have 22.04 Correctly Called Strikes a game with 28.02 as the Actual Strikes, averaging 5.98 missed strikes a game. By this measurement we can see that more hitter leniency was given to the away team than the home team.

During this time frame, while a higher percentage of strikes were judged correctly, hitters were given more leniency as the away team than the home team on a game-by-game basis.

Called Strike Likeliness in Specific Game Situation

Included in Table Eight are the three most and least likely umpires to call any non-fastball a strike below the vertical midpoint of our zone. I split the strike zone at 2.55 vertical feet and looked at any pitch (not necessarily within the zone) below that height. Here, we are not judging an umpire’s accuracy of correctly identifying pitches, but rather looking at where a certain umpire may call specific pitches. We can see that Doug Eddings is 5.34% more likely to call a strike on a non-fastball as compared to Carlos Torres.

While this does not paint the entire picture, we are able to see how their tendencies can play an important role in the game. Information like this may be valuable to a team in deciding how to pitch a specific batter, which reliever to bring into a game, or factor into being more patient or aggressive while at the plate.

Conclusion

External pressures and increased standards are undoubtable effects on umpire strike zones. As evidenced throughout this paper, strike zones are called smaller than the rule-book strike zone specifies. And while umpires are trending toward correctly identifying strikes, situations such as count and pitch type can affect their judgment.

While the system in place is not 100%, we must understand that these umpires are judging the fastest and most visually-deceptive pitches in the world and are the best at what they do. Major League Baseball must use modern technology to their advantage and provide the best training for umpires to achieve the goal of calling the rule-book strike zone. Another option, while more drastic and difficult to implement, may include adapting the definition of the rule-book strike zone, something that has not been changed since 1996.

The Phillies are a remarkable 9-9 in what’s now 18 games in to the 2017 season.  Why is that remarkable?  Because of what they’re doing on both sides of the ball in the late innings.

The team, as a whole, has an ERA in the ninth inning of 7.36.  Batters they’re facing in the ninth are OPS’ing a ridiculous .910 in the inning and the team has given up six home runs, nearly one in every ten plate appearances.  By almost every metric, the ninth inning has been the worst for Phillies pitching.  Only two teams have a worse ERA in the ninth and only three have a higher OPS.  Of the two teams with a higher ERA, the Rangers gave up half of their ninth inning runs in two games, and no teams have given up as many home runs.

With those kinds of ninth-inning numbers you’d expect that the Phillies would have a high amount of losses attributed to blown saves.  Of their 18 games so far, eight have come down to save situations, and they’ve given up runs in six of those games and blown four of them.  What’s remarkable, though, is that they’ve only lost two of those games, and both games were where the Phillies didn’t get a chance to bat following the blown save.  In the other two they’ve managed to come back and win.

So, how is it that they’ve managed to be at .500 over the first 18 games to start the season?  Well, there’s some interesting anomalies in the late innings on the offensive side as well.

To counter the poor pitching in the 9th inning, the Phillies batters are excelling at hitting in the late innings.  Through innings 7-9 as a team the Phillies are hitting .267/.336/.497 for an OPS of .834, which puts them at the best in baseball.  To put that in perspective, that’s extremely close to what Edwin Encarnacion hit all of last year, .263/.357/.529 for an .886 OPS.  Encarnacion finished 14^th in MVP voting.

What can be interpreted from this is that the Phillies are doing something all good teams seem to do – take advantage of relief pitching.  Indeed, their line against relievers so far this year is incredible at .282/.355/.531 for an OPS of .878, which is again the best in baseball.  Mets slugger Yoenis Cespedes earned himself a contract with an annual average value of $27.5M over the next four years by hitting similarly to what the Phillies are doing to relief pitchers early on this year.  He had a slash line of .280/.354/.530 for an OPS of .884.

For a team like the Phillies have been so far, every run seems to matter.  This isn’t a situation where they’re scoring extra runs or giving up meaningless runs in blowouts.  Over their 18 games they’re carrying a run differential of +7 runs.  To drive home how important every run is in a typical Phillies game, 13 of their 18 games have been decided by less than two runs, and nine have been one-run games.

So to say that the late innings for the Phillies have been adventurous is a bit of an understatement.  They’re giving up runs, but they’re scoring runs as well.  Surely this is a somewhat unsustainable balancing act, but due to the fact that it’s happening on both sides of the ball, when it does in fact even itself out, the end results aren’t likely to be much different.

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the teams with the biggest single-season difference in the WAR and Win Shares for the “Original” vs. “Actual” rosters for every Major League organization. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

AWAR – Wins Above Replacement for players on “actual” teams

AWS – Win Shares for players on “actual” teams

APW% – Pythagorean Won-Loss record for the “actual” teams

Assessment

The 2003 Cleveland Indians 

OWAR: 41.6     OWS: 262     OPW%: .500     (81-81)

AWAR: 26.7      AWS: 204     APW%: .420     (68-94)

WARdiff: 14.9                        WSdiff: 58  

The “Original” 2003 Indians came within one game of the American League Central Division title as the White Sox held off the Tribe and the Twins. Jim Thome launched a League-leading 47 moon-shots and drove in a career-best 131 baserunners. He scored 111 runs, drew 111 bases on balls and earned his highest finish in the MVP balloting (fourth). Manny Ramirez scorched the opposition with a .325 BA, 37 wallops, 104 ribbies, 117 runs scored and a League-best OBP of .427. Richie Sexson (.272/45/124) matched his career-best in home runs and fell one short of his top RBI mark. Brian S. Giles suffered a drop in production from his previous four campaigns but still managed to belt 20 long balls while posting a .299 BA.  “The Mayor” Sean Casey hit at a .291 clip but otherwise failed to deliver the power output expected from a first baseman. The lineup for the “Actual” 2003 Indians featured Milton Bradley (.321/10/56) and rookie outfielder Jody Gerut (.279/22/75).

Omar Vizquel (61^st-SS) and Ellis Burks (77^th-CF) placed in the top 100 player rankings according to “The New Bill James Historical Baseball Abstract among members of the “Actuals” roster.

  Original 2003 Indians                               Actual 2003 Indians

Bartolo Colon (15-13, 3.87) fashioned a WHIP of 1.198 and topped the American League with 9 complete games. Six-time All-Star lefthander C.C. Sabathia (13-9, 3.60) appeared in his first Mid-Summer Classic. David Riske notched 8 saves and a 0.964 WHIP along with a personal-best 2.29 ERA. Danys Baez (3.81, 25 SV) and Julian Tavarez (3.60, 11 SV) bolstered the relief corps.

  Original 2003 Indians                            Actual 2003 Indians  

Notable Transactions

Jim Thome 

October 28, 2002: Granted Free Agency.

December 6, 2002: Signed as a Free Agent with the Philadelphia Phillies. 

Manny Ramirez

October 27, 2000: Granted Free Agency.

December 19, 2000: Signed as a Free Agent with the Boston Red Sox.

Richie Sexson

July 28, 2000: Traded by the Cleveland Indians with a player to be named later, Kane Davis and Paul Rigdon to the Milwaukee Brewers for Jason Bere, Bob Wickman and Steve Woodard. The Cleveland Indians sent Marco Scutaro (August 30, 2000) to the Milwaukee Brewers to complete the trade. 

Brian S. Giles

November 18, 1998: Traded by the Cleveland Indians to the Pittsburgh Pirates for Ricardo Rincon.

Bartolo Colon 

June 27, 2002: Traded by the Cleveland Indians with Tim Drew to the Montreal Expos for Cliff Lee, Brandon Phillips, Grady Sizemore and Lee Stevens. 

Sean Casey 

March 30, 1998: Traded by the Cleveland Indians to the Cincinnati Reds for Dave Burba.

Honorable Mention

The 1941 Cleveland Indians 

OWAR: 43.0     OWS: 267     OPW%: .545     (84-70)

AWAR: 34.9      AWS: 225     APW%: .487     (75-79)

WARdiff: 8.1                        WSdiff: 42  

Engaged in heated combat with the Red Sox and Yankees down the stretch in ’41, the Tribe emerged in third place, four games behind Boston. Thornton Lee (22-11, 2.37) topped the Junior Circuit in ERA, WHIP (1.165) and complete games (30) to merit his lone All-Star invitation. Bob Feller (25-13, 3.15) led the League in victories, starts (40), shutouts (6) and innings pitched (343). “Rapid Robert” paced the AL in strikeouts for the fourth consecutive season and placed third in the MVP voting. Jeff Heath (.340/24/123) established career-highs in base hits (199), triples (20), RBI and stolen bases (18) while making his first All-Star appearance. “Old Reliable” Tommy Henrich clubbed a career-best 31 round-trippers and registered 106 tallies. Ken Keltner rapped 31 doubles, 13 triples and 23 circuit clouts. “Old Shufflefoot” Lou Boudreau socked 45 two-baggers and scored 95 runs.

On Deck

What Might Have Been – The “Original” 2010 Orioles

References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at “www.retrosheet.org”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive