Archive for the ‘Shifts’ Category

Many people don’t realize that one of the (many) weaknesses of UZR, at least for the infield, is that it ignores any ground ball in which the infield was configured in some kind of a “shift” and it “influenced the play.” I believe that’s true of DRS as well.

What exactly constitutes “a shift” and how they determine whether or not it “influenced the play” I unfortunately don’t know. It’s up to the “stringers” (the people who watch the plays and input and categorize the data) and the powers that be at Baseball Info Solutions (BIS). When I get the data, there is merely a code, “1” or “0”, for whether there was a “relevant shift” or not.

How many GB are excluded from the UZR data? It varies by team, but in 2015 so far, about 21% of all GB are classified by BIS as “hit into a relevant shift.” The average team has had 332 shifts in which a GB was ignored by UZR (and presumably DRS) and 1268 GB that were included in the data that the UZR engine uses to calculate individual UZR’s. The number of shifts varies considerably from team to team, with the Nationals, somewhat surprisingly, employing the fewest, with only 181, and the Astros with a whopping 682 so far this season. Remember these are not the total number of PA in which the infield is in a shifted configuration. These are the number of ground balls in which the infield was shifted and the outcome was “relevant to the shift,” according to BIS. Presumably, the numbers track pretty well with the overall number of times that each team employs some kind of a shift. It appears that Washington disdains the shift, relatively speaking, and that Houston loves it.

In 2014, there were many fewer shifts than in this current season. Only 11% of ground balls involved a relevant shift, half the number than in 2015. The trailer was the Rockies, with only 92, and the leader, the Astros, with 666. The Nationals last year had the 4th fewest in baseball.

Here is the complete data set for 2014 and 2015 (as of August 30):


Team GB Shifted Not shifted % Shifted
ari 2060 155 1905 8
atl 1887 115 1772 6
chn 1958 162 1796 8
cin 1938 125 1813 6
col 2239 92 2147 4
hou 2113 666 1447 32
lan 2056 129 1927 6
mil 2046 274 1772 13
nyn 2015 102 1913 5
phi 2105 177 1928 8
pit 2239 375 1864 17
sdn 1957 133 1824 7
sln 2002 193 1809 10
sfn 2007 194 1813 10
was 1985 116 1869 6
mia 2176 125 2051 6
ala 1817 170 1647 9
bal 1969 318 1651 16
bos 1998 247 1751 12
cha 2101 288 1813 14
cle 2003 265 1738 13
det 1995 122 1873 6
kca 1948 274 1674 14
min 2011 235 1776 12
nya 1902 394 1508 21
oak 1980 244 1736 12
sea 1910 201 1709 11
tba 1724 376 1348 22
tex 1811 203 1608 11
tor 1919 328 1591 17



Team GB Shifted Not shifted % Shifted
ari 1709 355 1354 21
atl 1543 207 1336 13
chn 1553 239 1314 15
cin 1584 271 1313 17
col 1741 533 1208 31
hou 1667 682 985 41
lan 1630 220 1410 13
mil 1603 268 1335 17
nyn 1610 203 1407 13
phi 1673 237 1436 14
pit 1797 577 1220 32
sdn 1608 320 1288 20
sln 1680 266 1414 16
sfn 1610 333 1277 21
was 1530 181 1349 12
mia 1591 229 1362 14
ala 1493 244 1249 16
bal 1554 383 1171 25
bos 1616 273 1343 17
cha 1585 230 1355 15
cle 1445 335 1110 23
det 1576 349 1227 22
kca 1491 295 1196 20
min 1655 388 1267 23
nya 1619 478 1141 30
oak 1599 361 1238 23
sea 1663 229 1434 14
tba 1422 564 858 40
tex 1603 297 1306 19
tor 1539 398 1141 26


The individual fielding data (UZR) for infielders that you see on Fangraphs is based on non-shifted ground balls only, or on ground balls where there was a shift but it wasn’t relevant to the outcome. The reason that shifts are ignored in UZR (and DRS, I think) is because we don’t know where the individual fielders are located. It could be a full shift, a partial shift, the third baseman could be the left-most fielder as he usually is or he could be the man in short right field between the first baseman and the second baseman, etc. The way most of the PBP defensive metrics work, it would be useless to include this data.

But what we can do, with impunity, is to include all ground ball data in a team UZR. After all, if a hard ground ball is hit at the 23 degree vector, and we are only interested in team fielding, we don’t care who is the closest fielder or where he is playing. All we care about is whether the ball was turned into an out, relative to the league average out rate for a similar ground ball in a similar or adjusted for context. In other words, using the same UZR methodology, we can calculate a team UZR using all ground ball data, with no regard for the configuration of the IF on any particular play. And if it is true that the type, number and timing (for example, against which batters and/or with which pitchers) of shifts is relevant to a team’s overall defensive efficiency, team UZR in the infield should reflect not only the sum of individual fielding talent and performance, but also the quality of the shift in terms of hit prevention. In addition, if we subtract the sum of the individual infielders’ UZR on non-shift plays from the total team UZR on all plays, the difference should reflect, at least somewhat, the quality of the shifts.

I want to remind everyone that UZR accounts for several contexts. One, park factors. For infield purposes, although the dimensions of all infields are the same, the hardness and quality of the infield can differ from park to park. For example, in Coors Field in Colorado and Chase Field in Arizona, the infields are hard and quick, and thus more ground balls scoot through for hits even if they leave the bat with the same speed and trajectory.

Two, the speed of the batter. Obviously faster batters require the infielders to play a little closer to home plate and they beat out infield ground balls more often than slower batters. In some cases the third baseman and/or first baseman have to play in to protect against the bunt. This affects the average “caught percentage” for any given category of ground balls. The speed of the opposing batters tends to even out for fielders and especially for teams, but still, the UZR engine tries to account for this just in case it doesn’t, especially in small samples.

The third context is the position of the base runners and number of outs. This affects the positioning of the fielders, especially the first baseman (whether first base is occupied or not). The handedness of the batters is the next context. As with batter speed, these also tend to even out in the long run, but it is better to adjust for them just in case.

Finally, the overall GB propensity of the pitchers is used to adjust the average catch rates for all ground balls. The more GB oriented a pitcher is, the softer his ground balls are. While all ground balls are classified in the data as soft, medium, or hard, even within each category, the speed and consequently the catch rates, vary according to the GB tendencies of the pitcher. For example, for GB pitchers, their medium ground balls will be caught at a higher rate than the medium ground balls allowed by fly ball pitchers.

So keep in mind that individual and team UZR adjust as best as it can for these contexts. In most cases, there is not a whole lot of difference between the context adjusted UZR numbers and the unadjusted ones. Also keep in mind that the team UZR numbers you see in this article are adjusted for park, batter hand and speed, and runners/outs, the same as the individual UZR’s you see on Fangraphs.

For this article, I am interested in team UZR including when the IF is shifted. Even though we are typically interested in individual defensive performance and talent, it is becoming more and more difficult to evaluate individual fielding for infielders, because of the prevalence of the shift, and because there is so much disparity in how often each team employs the shift (so that we might be getting a sample of only 60% of the available ground balls for one team and 85% for another).

One could speculate that teams that employ the most shifts would have the best team defense. To test that, we could look at each team’s UZR versus their relevant shift percentage. The problem, of course, is that the talent of the individual fielders is a huge component of team UZR, regardless of how often a team shifts. There may also be selective sampling going on. Maybe teams that don’t have good infield defense feel the need to shift more often such that lots of shifts get unfairly correlated with (but are not the cause of) bad defense.

One way we can separate out talent from shifting is to compare team UZR on all ground balls with the total of the individual UZR’s for all the infielders (on non-shifted ground balls). The difference may tell us something about the efficacy of the shifts and non-shifts. In other words, total team individual infield UZR, which is just the total of each infielder’s UZR as you would see on Fangraphs (range and ROE runs only), represents what we generally consider to be a sample of team talent. This is measured on non-shifted ground balls only, as explained above.

Team total UZR, which measures team runs saved or cost, with no regard for who caught each ball or not, and is based on every batted ball, shifted or not, represents how the team actually performed on defense and is a much better measure of team defense than totaling the individual UZR’s. The difference, then, to some degree, represents how efficient teams are at shifting or not shifting, regardless of how often they shift.

There are lots of issues that would have to be considered when evaluating whether shifts work or not. For example, maybe shifting too much with runners on base results in fewer DP because infielders are often out of position. Maybe stolen bases are affected for the same reason. Maybe the number and quality of hits to the outfield change as a result of the shift. For example, if a team shifts a lot, maybe they don’t appear to record more ground ball outs, but the shifted batters are forced to try and hit the ball to the opposite field more often and thus they lose some of their power.

Maybe it appears that more ground balls are caught, but because pitchers are pitching “to the shift” they become more predictable and batters are actually more successful overall (despite their ground balls being caught more often). Maybe shifts are spectacularly successful against some stubborn and pull-happy batters and not very successful against others who can adjust or even take advantage of a shift in order to produce more, not less, offense. Those issues are beyond the scope of UZR and this article.

Let’s now look at each team in 2014 and 2015, their shift percentage, their overall team UZR, their team UZR when shifting, when not shifting, and their total individual combined UZR when not shifting. Remember this is for the infield only.


Team % Shifts Shift Runs Non-Shift Runs Team Runs Total Individual Runs Team Minus Individual after prorating Ind Runs to 100% of plays
KCA 20 -2.2 10.5 10 26.3 -19.6
LAN 13 -5 -7.3 -13.3 0.8 -14.2
TOR 26 -2.5 13.9 11 22.6 -15.6
CHA 15 -7.7 -12.3 -21.8 -11.9 -8.9
CLE 23 0.6 3.3 3.3 12.8 -11.4
MIN 23 3.5 -11.6 -7.6 1.8 -9.7
MIL 17 0.3 -7.1 -6.7 2.5 -9.5
SEA 14 -2.6 -8.7 -13.8 -5.1 -8.3
SFN 21 2.3 12.6 15.8 24.4 -11.8
MIA 14 0.5 2.7 2.4 8.4 -6.7
ARI 21 3.4 -1.5 2.1 8 -7.0
HOU 41 -7.6 -3.2 -11.3 -6.1 -3.1
PHI 14 -6.4 -16.4 -23.5 -19 -3.0
COL 31 -7.3 0 -5.5 -1.5 -3.7
ATL 13 3.1 6.9 9.8 12.6 -3.7
SLN 16 -1.1 -5.8 -8.8 -7 -1.1
DET 22 1.8 -16.2 -17.8 -16 0.5
ALA 16 -2.4 -0.4 -3.6 -2.8 -0.5
BOS 17 0.3 4.8 3.5 2.7 0.5
NYN 13 -3.8 3.1 0.8 -2.7 3.7
WAS 12 1.1 -9.4 -8.4 -12.6 5.1
CIN 17 5 9.8 16.2 11.2 3.9
CHN 15 0.2 18.7 17.4 10.5 6.0
BAL 25 10.6 -0.5 14.4 5.8 7.6
SDN 20 7.5 -6.8 1.5 -7.8 10.3
TEX 19 4.1 12.8 19.6 10.1 8.3
TBA 40 0.1 4.5 7 -9.2 19.3
NYA 30 0.1 11.8 12.2 -6.6 20.2
PIT 32 0.3 0.3 0.1 -21 26.0
OAK 23 3.9 -8.8 -5 -31.4 31.1


The last column, as I explained above, represents the difference between how we think the infield defense performed based on individual UZR’s only (on non-shifted GB), prorated to 100% of the games (the proration is actually regressed so that we don’t have the “on pace for” problem), and how the team actually performed on all ground balls. If the difference is positive, then we might assume that the shifts and non-shifts are being done in an effective fashion regardless of how often shifts are occurring. If it is negative, then somehow the combination of shifts and non-shifts are costing some runs. Or the difference might not be meaningful at all – it could just be noise. At the very least, this is the first time that you are seeing real infield team defense being measured based on the characteristics of each and every ground ball and the context in which they were hit, regardless of where the infielders are playing.

First of all, if we look at all the teams that have a negative difference in the last column, the teams that presumably have the worst shift/no-shift efficiency, and compare them to those that are plus and presumably have the best shift/no-shift efficiency, we find that there is no difference in their average shift percentages. For example, TBA and HOU have the most shifts by far, and HOU “cost” their teams 5.2 runs and TBA benefited by 16.2 runs. LAN and WAS had the fewest shifts and one of them gained 4 runs and the other lost 14 runs.  The other teams are all over the board with respect to number of shifts and the difference between the individual UZR’s and team UZR.

Let’s look at that last column for 2014 and compare it to 2015 to see if there is any consistency from year to year within teams. Do some teams consistently do better or worse with their shifting and non-shifting, at least for 2014 and 2015? Let’s also see if adding more data gives us any relationship between the last column (delta team and individual UZR) and shift percentage.

Team 2015 % Shift 2014 % Shift 2015 Team Minus Individual 2014 Team Minus Individual Combined 2014 and 2015 Team Minus Individual
HOU 41 32 -5.2 45.6 40.4
TBA 40 22 16.2 12.7 28.9
PIT 32 17 21.1 5.5 26.6
TEX 19 11 9.5 9.9 19.4
WAS 12 6 4.2 13.0 17.2
OAK 23 12 26.4 -9.3 17.1
BAL 25 16 8.6 7.6 16.2
NYN 13 5 3.5 9.0 12.5
NYA 30 21 18.8 -8.4 10.4
CHA 15 14 -9.9 12.5 2.6
CHN 15 8 6.9 -5.8 1.1
TOR 26 17 -11.6 12.6 1.0
DET 22 6 -1.8 2.4 0.6
SFN 21 10 -8.6 6.0 -2.6
CIN 17 6 5 -8.2 -3.2
CLE 23 13 -9.5 5.2 -4.3
MIL 17 13 -9.2 3.1 -6.1
ARI 21 8 -5.9 -0.2 -6.1
SDN 20 7 9.3 -15.7 -6.4
MIA 14 6 -6 -0.9 -6.9
BOS 17 12 0.8 -10.6 -9.8
KCA 20 14 -16.3 6.3 -10.0
ATL 13 6 -2.8 -7.5 -10.3
PHI 14 8 -4.5 -6.2 -10.7
ALA 16 9 -0.8 -11.6 -12.4
SLN 16 10 -1.8 -12.2 -14.0
LAN 13 6 -14.1 -2.5 -16.6
MIN 23 12 -9.4 -9.3 -18.7
SEA 14 11 -8.7 -11.3 -20.0
COL 31 4 -4 -23.0 -27.0


Although there appears to be little correlation from one year to the next for each of the teams, we do find that of the teams that had the least efficient shifts/non-shifts (negative values in the last column), they averaged 14% shifts per season in 2014 and 2015. Those that had the most effective (plus values in the last column) shifted an average of 19% in 2014 and 2015. As well, the two teams with the biggest gains, HOU and TB, had the most shifts, at 37% and 31% per season, respectively. The two worst teams, Colorado and Seattle, shifted 17% and 13% per season. On the other hand, the team with the least shifts in baseball in 2014 and 2015 combined, the Nationals, gained over 17 runs in team UZR on all ground balls compared to a total of the individual UZR’s on non-shifted balls only, suggesting that the few shifts they employed were very effective, which seems reasonable.

It is also interesting to note that the team that had the worst difference in team and individual UZR in 2014, the Rockies, only shifted 4% of the time, easily the worst in baseball. In 2015, they have been one of the most shifted teams and still their team UZR is 4 runs worse than their total individual UZR’s. Still, that’s a lot better than in 2014.

It also appears that many of the smarter teams are figuring out how to optimize their defense beyond the talent of the individual players. TB, PIT, HOU, WAS, and OAK are at the top of the list in plus value deltas (the last column). These teams are generally considered to have progressive front offices. Some of the teams with the most negative numbers in the last column, those teams which appear to be sub-optimal in their defensive alignment, are LAN, MIN, SEA, PHI, COL, ATL, SLN, and ALA, all with reputations for having less than progressive front offices and philosophies, to one degree or another. In fact, other than a few outliers, like Boston, Texas, and the White Sox, the order of the teams in the chart above looks like a reasonable order of teams from most to least progressive teams. Certainly the teams in the top half appear to be the most saber-savvy teams and those in bottom half, the least.

In conclusion, it is hard to look at this data and figure out whether and which teams are using their shifts and non-shifts effectively. There doesn’t appear to be a strong correlation between shift percentage and the difference between team and individual defense although there are a few anecdotes that suggest otherwise. As well, in the aggregate for 2014 and 2015 combined, teams that have been able to outperform on team defense the total of their individual UZR’s have shifted more often, 19% to 13%.

There also appears to the naked eye to be a strong correlation between the perceived sabermetric orientation of a team’s front office and the efficiency of their shift/non-shift strategy, at least as measured by the numbers in the last column, explained above.

I think the most important thing to take away from this discussion is that there can be a fairly large difference between team infield UZR which uses every GB, and the total of the individual UZR’s which uses only those plays in which no shift was relevant to the outcome of the play. As well, the more shifts employed by a team, the less we should trust that the total of the individual performances are representative of the entire team’s defense on the infield. I am also going to see if Fangraphs can start publishing team UZR for infielders and for outfielders, although in the outfield, the numbers should be similar if not the same.


I just downloaded my Kindle version of the brand spanking new Hardball Times Annual, 2014 from It is also available from (best place to order).

Although I was disappointed with last year’s Annual, I have been very much looking forward to reading this year’s, as I have enjoyed it tremendously in the past, and have even contributed an article or two, I think. To be fair, I am only interested in the hard-core analytical articles, which comprise a small part of the anthology. The book is split into 5 parts, according to the TOC: The “2013 season,” which consists of reviews/views of each of the six divisions plus one chapter about the post-season. Two, general Commentary. Three, History, four, Analysis, and finally, a glossary of statistical terms, and short bios on the various illustrious authors (including Bill James and Rob Neyer).

As I said, the only chapters which interest me are the ones in the Analysis section, and those are the ones that I am going to review, starting with Jeff Zimmerman’s, “Shifty Business, or the War Against Hitters.” It is mostly about the shifts employed by infielders against presumably extreme pull (and mostly slow) hitters. The chapter is pretty good with lots of interesting data mostly provided by Inside Edge, a company much like BIS and STATS, which provides various data to teams, web sites, and researchers (for a fee). It also raised several questions in my mind, some of which I wish Jeff had answered or at least brought up himself. There were also some things that he wrote which were confusing – at least in my 50+ year-old mind.

He starts out, after a brief intro, with a chart (BTW, if you have the Kindle version, unless you make the font size tiny, some of the charts get cut off) that shows the number, BABIP, and XBH% of plays where a ball was put into play with a shift (and various kinds of shifts), no shift, no doubles defense (OF deep and corners guarding lines), infield in, and corners in (expecting a bunt). This is the first time I have seen any data with a no-doubles defense, infield in, and with the corners up anticipating a bunt. The numbers are interesting. With a no-doubles defense, the BABIP is quite high and the XBH% seems low, but unfortunately Jeff does not give us a baseline for XBH% other than the values for the other situations, shift, no shift, etc., although I guess that pretty much includes all situations. I have not done any calculations, but the BABIP for a no-doubles defense is so high and the reduction in doubles and triples is so small, that it does not look like a great strategy off the top of my head. Obviously it depends on when it is being employed.

The infield-in data is also interesting. As expected, the BABIP is really elevated. Unfortunately, I don’t know if Jeff includes ROE and fielder’s choices (with no outs) in that metric. What is the standard? With the infield in, there are lots of ROE and lots of throws home where no out is recorded (a fielder’s choice). I would like to know if these are included in the BABIP.

For the corners playing up expecting a bunt, the numbers include all BIP, mostly bunts I assume. It would have been nice had he given us the BABIP when the ball is not bunted (and bunted). An important consideration for whether to bunt or not is how much not bunting increases the batter’s results when he swings away.

I would also have liked to see wOBA or some metric like that for all situations – not just BABIP and XBH%. It is possible, in fact likely, that walk and K rates vary in different situations. For example, perhaps walk rates increase when batters are facing a shift because they are not as eager to put the ball in play or the pitchers are trying to “pitch into the shift” and are consequently more wild. Or perhaps batters hit more HR because they are trying to elevate the ball as opposed to hitting a ground ball or line drive. It would also be nice to look at GDP rates with the shift. Some people, including Bill James, have suggested that the DP is harder to turn with the fielders out of position. Without looking at all these things, it is hard to say that the shift “works” or doesn’t work just by looking at BABIP (and even harder to say to what extent it works).

Jeff goes on to list the players against whom the shift is most often employed. He gives us the shift and no shift BABIP and XBH%. Collectively, their BABIP fell 37 points with the shift and it looks like their XBH% fell a lot too (although for some reason, Jeff does not give us that collective number, I don’t think). He writes:

…their BABIP [for these 20 players] collectively fell 37 points…when hitting with the shift on. In other words, the shift worked.

I am not crazy about that conclusion – “the shift worked.” First of all, as I said, we need to know a lot more than BABIP to conclude that “the shift worked.” And even if it did “work” we really want to know by how much in terms of wOBA or run expectancy. Nowhere is there an attempt by Jeff to do that. 37 points seems like a lot, but overall it could be only a small advantage. I’m not saying that it is small – only that without more data and analysis we don’t know.

Also, when and why are these “no-shifts” occurring? Jeff is comparing shift BIP data to no-shift BIP data and he is assuming that everything else is the same. That is probably a poor assumption. Why are these no-shifts occurring? Probably first and foremost because there are runners on base. With runners on base, everything is different. It might also be with a completely different pool of pitchers and fielders. Maybe teams are mostly shifting when they have good fielders? I have no idea. I am just throwing out reasons why it may not be an apples-to-apples comparison when comparing “shift” results to “no-shift” results.

It is also likely that the pool of batters is different with a shift and no shift even though he only looked at the batters who had the most shifts against them. In fact. a better method would have been a “delta” method, whereby he would use a weighted average of the differences between shift and no-shift for each individual player.

He then lists the speed score and GB and line drive pull percentages for the top ten most shifted players. The average Bill James speed score was 3.2 (I assume that is slow, but again, I don’t see where he tells us the average MLB score), GB pull % was 80% and LD pull % was 62%. The average MLB GB and LD pull %, Jeff tells us, is 72% and 50%, respectively. Interestingly several players on that list were at or below the MLB averages in GB pull %. I have no idea why they are so heavily shifted on.

Jeff talks a little bit about some individual players. For example, he mentions Chris Davis:

“Over the first four months of the season, he hit into an average of 29 shifts per month, and was able to maintain a .304 BA and a .359 BABIP. Over the last two months of the season, teams shifted more often against him…41 times per month. Consequently, his BA was .250 and his BABIP was .293.

The shift was killing him. Without a shift employed, Davis hit for a .425 BABIP…over the course of the 2013 season. When the shift was set, his BABIP dropped to .302…

This reminds me a little of the story that Daniel Kahneman, 2002 Nobel Prize Laureate in Economics, tells about teaching military flight instructors that praise works better than punishment. One of the instructors said:

“On many occasions I have praised flight cadets for clean execution of some aerobatic maneuver, and in general when they try it again, they do worse. On the other hand, I have often screamed at cadets for bad execution, and in general they do better the next time.”

Of course the reason for that was “regression towards the mean.” No matter what you say to someone who has done poorer than expected, they will tend to do better next time, and vice versa for someone who has just done better than expected.

If Chris Davis hits .304 the first four months of the season with a BABIP of .359, and his career numbers are around .260 and .330, then no matter what you do against him (wear your underwear backwards, for example), his next two months are likely going to show a reduction in both of these numbers! That does not necessarily imply a cause and effect relationship.

He makes the same mistake with several other players that he discusses. I fact, I have always had the feeling that at least part of the “observed” success for the shift was simply regression towards the mean. Imagine this scenario – I’m not saying that this is exactly what happens or happened, but to some extent I think it may be true. You are a month into the season and for X number of players, say they are all pull hitters, they are just killing you with hits to the pull side. Their collective BA and BABIP is .380 and .415. You decide enough is enough and you decide to shift against them. What do you  think is going to happen and what do you think everyone is going to conclude about the effectiveness of the shift, especially when they compare the “shift” to “no-shift” numbers?

Again, I think that the shift gives the defense a substantial advantage. I am just not 100% sure about that and I am definitely not sure about how much of an advantage it is and whether it is correctly employed against every player.

Jeff also shows us the number of times that each team employs the shift. Obviously not every team faces the same pool of batters, but the differences are startling. For example, the Orioles shifted 470 times and the Nationals 41! The question that pops into my mind is, “If the shift is so obviously advantageous (37 points of BABIP) why aren’t all teams using it extensively?” It is not like it is a secret anymore.

Finally, Jeff discusses bunting to beat the shift. That is obviously an interesting topic. Jeff shows that not many batters opt to do that but when they do, they reach base 58% of the time. Unfortunately, out of around 6,000 shifts where the ball was put into play, players only bunted 48 times! That is an amazingly low number. Jeff (likely correctly) hypothesizes that players should be bunting more often (a lot more often?). That is probably true, but I don’t think we can say how often and by whom? Maybe most of the players who did not bunt are terrible bunters and all they would be doing is bunting back to the pitcher or fouling the ball off or missing. And BTW, telling us that a bunt results in reaching base 58% of the time is not quite the whole story. We also need to know how many bunt attempts resulted in a strike. Imagine that if a player attempted to bunt 10 times, fouled it off or missed it 9 times and reached base once.  That is probably not a good result even though it looks like he bunted with a 1.000 average!

It is also curious to me that 7 players bunted into a shift almost 4 times each, and reached base 16 times (a .615 BA). They are obviously decent or good bunters. Why are they not bunting every time until the shift is gone against them? They are smart enough to occasionally bunt into a shift, but not smart enough to always do it? Something doesn’t seem right.

Anyway, despite my many criticisms, it was an interesting chapter and well-done by Jeff. I am looking forward to reading the rest of the articles in the Analysis section and if I have time, I will review one or more of them.