In Search of Clutch Hitting

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In Search Of Clutch Hitting

Introduction

Clutch hitting is back in fashion in the baseball research community. For years, many of us had looked in vain for the existence of some persistent clutch hitting ability and, failing to find it, had come to the conclusion that such an ability must not exist. The pioneer of this approach was Dick Cramer, who wrote an article on this subject in the 1977 Baseball Research Journal, but many of us have done similar studies over the years. First, you determine who performs better than normally one year in "clutch situations" (and the definitions of these situations change from study to study) and then you see if these players have a tendency to repeat their performance the next season. They don't, which has led a generation of baseball researchers to roll their eyes whenever announcers start rhapsodizing about Joe Blow's ability to come through when it counts.

In "Underestimating the Fog", an article in the 2005 Baseball Research Journal, Bill James argues that we were wrong to think that such an approach "proved" anything. There is so much random noise inherent in this method, so much "Fog", that we shouldn't expect to see anything when looking for clutch ability in this manner. Well, I might get around to testing this hypothesis at some point, but for now I thought I'd take a different tack. I thought it might be interesting to compare a player's ability in both clutch and non-clutch situations over the course of his career. So I'm not really looking for persistence in results from one year to the next, but rather I'll be looking for results that are not what we'd expect to see if there were only random forces at work. Hopefully, dealing with much larger groups of at-bats will help to thin out the fog somewhat.

Identifying "Clutch"

The first problem facing anyone undertaking a study like this is that we don't really know what clutch means. Or rather it seems to mean something different whenever it's used, depending upon the point we are trying to make. Ted Williams was once accused of not being a clutch player based upon his performance in a handful of games, selected both because they had a significant impact on his teams chances to win a World Championship and because he performed relatively poorly in them. Games in the middle of a tight pennant race weren't clutch, only a couple at the very end of a few seasons. Others have defined terms like "Late Inning Pressure Situations" to identify players who perform well or poorly in a handful of at-bats near the end of close games.

One lazy way out of this problem (hint: it's the one I'll be taking) is to define a clutch situation as an at-bat with runners in scoring position. In a sense, this is nonsense: a lead-off hitter in the late innings of a tie game is usually a much more clutch situation than a batter at the plate with a runner on second and a 15-2 lead. Still, it's often what we mean by "clutch". I don't know about you, but when someone talks about how well this or that player has hit in the clutch, I'll usually test the statement by checking to see how the man has hit with men in scoring position. These at-bats may not all be the most pressure-packed of the season, but they probably come close enough for our purposes.

Situational Biases

There is a problem with taking this approach, however: batters do not hit equally well in all situations. Here is a breakdown of batter stats for each of the 24 game situations from 1960 to 2004. Note: this data is not complete for these years and for the purposes of this article, we will be ignoring any games for which we are missing play-by-play information. For a list of these missing games by league and team, please see the article on the Value Added method.

FST  Out      AB       H     2B    3B     HR     BB   IBB   HBP    SF   OBP   SLG   OPS
---    0 1532622  397872  70352 10052  41281 124600     6  9597     0  .319  .399  .719
---    1 1089667  272485  47627  6764  26633  93420    24  6859     0  .313  .380  .693
---    2  855785  211439  37083  4936  21779  82275    91  5645     0  .317  .378  .696
x--    0  321722   91402  14907  1921   8458  21371     3  2354     0  .333  .421  .755
x--    1  396177  111618  18431  2313  10860  27732    36  2658     0  .333  .422  .755
x--    2  400916  103338  17374  2518  10836  32030   131  2531     0  .317  .395  .711
-x-    0   98402   24968   4104   654   1967  10171   621   862     6  .329  .369  .698
-x-    1  184504   45058   8137  1278   4269  30453  8618  1494    35  .356  .372  .727
-x-    2  218447   52285   9214  1509   5045  45860 17097  1757     0  .375  .365  .740
xx-    0   75884   20950   3420   411   2045   5327    16   660     7  .329  .413  .742
xx-    1  154245   40316   7178   961   4073  11667    52  1175    26  .318  .400  .718
xx-    2  199902   47270   8384  1422   4753  17909   168  1540     0  .304  .364  .668
--x    0   17713    5348    953   195    388   2276   181   178  2835  .339  .443  .783
--x    1   53374   17321   3045   514   1356   9344  2363   663  9639  .374  .477  .851
--x    2   90284   21777   3907   628   2075  16646  3975   799     0  .364  .367  .731
x-x    0   29489    9865   1653   192    865   2459   340   309  5351  .336  .492  .828
x-x    1   60054   19653   3278   460   1728   5483   786   598 11295  .332  .483  .816
x-x    2   91638   23143   4109   667   2179   8579   472   773     0  .322  .383  .705
-xx    0   16965    5153    920   165    437   3173  1304   203  2882  .367  .455  .822
-xx    1   36700   10873   1957   319    809  16311 11963   452  6302  .462  .433  .896
-xx    2   49866   11563   2089   347   1062  14106  7438   476     0  .406  .352  .757
xxx    0   19001    6248   1064   128    599   1367     0   216  3466  .326  .493  .818
xxx    1   48260   14911   2646   384   1406   3396     0   503  8762  .309  .467  .776
xxx    2   68338   16292   2938   553   1710   5491     1   529     0  .300  .373  .673
 
Where: AB  - at bats
       H   - hits
       2B  - doubles
       3B  - triples
       HB  - home runs
       BB  - walks
       IBB - intentional walks
       HBP - hit by pitch
       SF  - sacrifice flies
       OBP - on-base percentage (H + BB + hit by pitch) / (AB + BB + hit by pitch + SF)
       SLG - slugging percentage (H + 2B + (2 * 3B) + (3 * HR)) / AB
       OPS - on-base plus slugging percentage

I thought it might be easier to see some trends if I compressed the data in a few ways. First, the aggregate performance by outs:

Out      AB       H     2B    3B     HR     BB   IBB   HBP    SF   OBP   SLG   OPS
  0 2111798  561806  97373 13718  56040 170744  2471 14379 14547  .323  .405  .728
  1 2022981  532235  92299 12993  51134 197806 23843 14402 36059  .328  .397  .725
  2 1975176  487107  85098 12580  49439 222896 29373 14050     0  .327  .378  .705

And by men on:

FST      AB       H     2B    3B     HR     BB   IBB   HBP    SF   OBP   SLG   OPS
--- 3478074  881796 155062 21752  89693 300295   121 22101     0  .317  .388  .705
x-- 1118815  306358  50712  6752  30154  81133   170  7543     0  .327  .412  .739
-x-  501353  122311  21455  3441  11281  86484 26336  4113    41  .360  .368  .728
xx-  430031  108536  18982  2794  10871  34903   236  3375    33  .313  .385  .699
--x  161371   44446   7905  1337   3819  28266  6519  1640 12474  .365  .412  .777
x-x  181181   52661   9040  1319   4772  16521  1598  1680 16646  .328  .434  .762
-xx  103531   27589   4966   831   2308  33590 20705  1131  9184  .423  .397  .820
xxx  135599   37451   6648  1065   3715  10254     1  1248 12228  .307  .423  .730

And with runners in and out of scoring position:

               AB       H     2B    3B     HR     BB   IBB   HBP    SF   OBP   SLG   OPS
Not RISP  4596889 1188154 205774 28504 119847 381428   291 29644     0  .319  .394  .713
RISP      1513066  392994  68996 10787  36766 210018 55396 13187 50606  .345  .392  .737

With men in scoring position, batters have just about the same slugging percentage and a higher on-base percentage than they do in their other at-bats. So they hit somewhat better in these situations. Except, of course, that they don't.

There are two deceptive things about comparing situational statistics in this manner. First of all, sacrifice flies only occur with a lead runner on third or (very rarely) on second. These are about the same as run-scoring ground outs and the decision not to count these as at-bats is a mistake first adopted in 1889 (when, who knows, perhaps it made sense), a mistake which has gone in and out of fashion over the years. I'm not sure how many of the sacrifice flies hit from 1960 to 2004 were actually struck with a sacrificial intent, but I'd be surprised if all but a handful of these were merely failed attempts at getting a hit. So the first thing we're going to do in our study is to treat sacrifice flies as at-bats.

The second thing that's misleading is walks. Walk rates vary quite a bit from situation to situation. With men on second and third and one out, a batter is nearly five times more likely to get a walk than he is with the bases loaded and one out. Most of this difference is due to intentional walks, which are easy to remove (and we will), but large differences in walk rates still remain even without them. Not only are these differences significant, but they vary quite a bit from batter to batter. The reason for this, of course, is that in some situations (most frequently with first base open) some batters are "pitched around" and sent to first via a non-intentional intentional walk. The decision to pitch to a batter in this manner is largely made based upon the his reputation, the relative handedness of the pitcher and batter, and often simply because of a manager's hunch.

As a result, in addition to treating sacrifice flies as outs in this study, I'm also going to ignore walks. This is not to say that walks aren't important, or are not in many instances the outcome of "clutch" at-bats, only that it is difficult to level the playing field with respect to walks and I don't want a batter's reputation inflating (or deflating) his apparent performance in clutch situations.

And finally, I'm also going to remove hit by pitch. Not that it can't be clutch (and painful) to take one for the team, but I'd like to concentrate on the hitting aspect of batting, rather than the getting hit.

With these changes made, here are the new situational breakdowns:

FST  Out      AB       H     2B    3B     HR   AVG   SLG   BPS
---    0 1532622  397872  70352 10052  41281  .260  .399  .659
---    1 1089667  272485  47627  6764  26633  .250  .380  .630
---    2  855785  211439  37083  4936  21779  .247  .378  .625
x--    0  321722   91402  14907  1921   8458  .284  .421  .705
x--    1  396177  111618  18431  2313  10860  .282  .422  .704
x--    2  400916  103338  17374  2518  10836  .258  .395  .652
-x-    0   98408   24968   4104   654   1967  .254  .369  .622
-x-    1  184539   45058   8137  1278   4269  .244  .372  .616
-x-    2  218447   52285   9214  1509   5045  .239  .365  .604
xx-    0   75891   20950   3420   411   2045  .276  .413  .689
xx-    1  154271   40316   7178   961   4073  .261  .400  .661
xx-    2  199902   47270   8384  1422   4753  .236  .364  .600
--x    0   20548    5348    953   195    388  .260  .382  .643
--x    1   63013   17321   3045   514   1356  .275  .404  .679
--x    2   90284   21777   3907   628   2075  .241  .367  .609
x-x    0   34840    9865   1653   192    865  .283  .416  .699
x-x    1   71349   19653   3278   460   1728  .275  .407  .682
x-x    2   91638   23143   4109   667   2179  .253  .383  .636
-xx    0   19847    5153    920   165    437  .260  .389  .648
-xx    1   43002   10873   1957   319    809  .253  .370  .622
-xx    2   49866   11563   2089   347   1062  .232  .352  .583
xxx    0   22467    6248   1064   128    599  .278  .417  .695
xxx    1   57022   14911   2646   384   1406  .261  .395  .657
xxx    2   68338   16292   2938   553   1710  .238  .373  .611
 
Where: AB  - at bats plus sacrifice flies
       AVG - batting average (H / (AB + SF))
       SLG - slugging percentage (H + 2B + (2 * 3B) + (3 * HR)) / (AB + SF)
       BPS - batting average plus slugging percentage

The performance by outs:

Out      AB       H     2B    3B     HR   AVG   SLG   BPS
  0 2126345  561806  97373 13718  56040  .264  .402  .666
  1 2059040  532235  92299 12993  51134  .258  .390  .649
  2 1975176  487107  85098 12580  49439  .247  .378  .624

And by men on:

FST  Out      AB       H     2B    3B     HR   AVG   SLG   BPS
---      3478074  881796 155062 21752  89693  .254  .388  .642
x--      1118815  306358  50712  6752  30154  .274  .412  .686
-x-       501394  122311  21455  3441  11281  .244  .368  .612
xx-       430064  108536  18982  2794  10871  .252  .385  .638
--x       173845   44446   7905  1337   3819  .256  .382  .638
x-x       197827   52661   9040  1319   4772  .266  .398  .664
-xx       112715   27589   4966   831   2308  .245  .365  .610
xxx       147827   37451   6648  1065   3715  .253  .388  .641

And with runners in and out of scoring position:

               AB       H     2B    3B     HR   AVG   SLG   BPS
Not RISP  4596889 1188154 205774 28504 119847  .258  .394  .652
RISP      1563672  392994  68996 10787  36766  .251  .380  .631

So with these adjustments, it's clear that batters actually hit worse with runners in scoring than they do otherwise.

Since batters hit best with a man on first, I thought it might be interesting to see how right-handed and left-handed hitters do in these situations.

Righties:

 FST  Out      AB       H     2B    3B     HR  BAVG   SLG   BPS
 ---      2090674  523625  92577 11626  54934  .250  .385  .635
 x--       677801  181155  30450  3739  18246  .267  .404  .671
 -x-       300296   72633  12563  1876   6802  .242  .364  .606
 xx-       263504   65779  11630  1539   6715  .250  .382  .632
 --x       105321   26474   4667   733   2358  .251  .377  .628
 x-x       120675   31523   5479   714   2932  .261  .391  .653
 -xx        70013   17130   3107   482   1490  .245  .367  .611
 xxx        92149   22948   4151   579   2244  .249  .380  .629

Lefties:

 FST  Out      AB       H     2B    3B     HR  BAVG   SLG   BPS
 ---      1387400  358171  62485 10126  34759  .258  .393  .651
 x--       441014  125203  20262  3013  11908  .284  .425  .708
 -x-       201098   49678   8892  1565   4479  .247  .374  .621
 xx-       166560   42757   7352  1255   4156  .257  .391  .647
 --x        68524   17972   3238   604   1461  .262  .391  .653
 x-x        77152   21138   3561   605   1840  .274  .407  .681
 -xx        42702   10459   1859   349    818  .245  .362  .607
 xxx        55678   14503   2497   486   1471  .260  .402  .663

As expected, left-handed hitters are able to take more advantage of the man on first situation, since holding the runner on opens up a hole on the right side.

Since I want to do away with as much of the fog as possible in this study, I'm going to only consider those players with at least 3000 at-bats (including sacrifice flies). This group of players should be significantly better hitters than the ones with less than 3000 at-bats for two reasons. First of all, requiring a significant number of at-bats will eliminate all pitchers from the mix. And secondly, I'm assuming that batters with longer careers are better than those with shorter careers.

Before going much further, then, I wanted to see if my target group showed a similar decline with runners in scoring position. Here are the statistics for the two groups of batters:

 1-2999        AB       H     2B    3B     HR  BAVG   SLG   BPS
Not RISP  1748765  414965  70688  9459  35513  .237  .349  .587
RISP       588724  135614  23479  3614  10466  .230  .336  .566
 
 >=3000        AB       H     2B    3B     HR  BAVG   SLG   BPS
Not RISP  2848124  773189 135086 19045  84334  .271  .421  .693
RISP       974948  257380  45517  7173  26300  .264  .406  .670

The percentage declines were about the same for the two groups. This isn't what I would have expected if clutch hitting is a talent that some players have and others don't. I would have assumed that the more talented group of hitters would have done better. Of course, there's no reason why talent and clutch ability have to go hand in hand.

Still, I was surprised that batters, both good and bad, hit worse with men in scoring position. Much of this is due to the big spike in performance that occurs when there's a man on first. Another reason is the presence of force-outs and fielder choices that aren't available with no one on.

Still, the single worst hitting situation is second and third with two outs. One reason for this could be a selection bias: good hitters are often walked in these situations. As a result, the quality of hitters batting at these times is lower than at others. I thought this might be something we could look at. Here are the average BPSs of the hitters up in each of the 24 game situations:

MenOn       Number of Outs
 FST       0       1       2
 ---     .646    .640    .647
 x--     .655    .659    .647
 -x-     .656    .651    .648
 xx-     .664    .652    .634
 --x     .648    .662    .651
 x-x     .664    .658    .642
 -xx     .656    .643    .636
 xxx     .651    .641    .630

There's something to this theory, as the quality of hitters at the plate with men on second and third and two out is the among the worst.

Later on, we will explore some other possible explanations for the dropoff in performance with runners in scoring position.

The Players

Enough talk. So who were the greatest clutch hitters from 1960 to 2004, the players who were able to raise the level of their game when it mattered most (or at least when runners were on second or third)? Here they are:

                           - NO-RISP -  --- RISP --
Name              B    AB     AB   BPS     AB   BPS    DIFF
Bill Spiers       L  3430   2548  .607    882  .722    .115
Mike Sweeney      R  3760   2673  .764   1087  .867    .103
Pat Tabler        R  3948   2815  .626   1133  .725    .099
Jose Valentin     B  4882   3678  .666   1204  .765    .099
Wayne Garrett     L  3308   2557  .557    751  .643    .087
Sandy Alomar      B  4748   3831  .519    917  .592    .073
Tony Fernandez    B  7972   6100  .665   1872  .736    .071
Rennie Stennett   R  4554   3520  .612   1034  .682    .070
Joe Girardi       R  4150   3117  .596   1033  .666    .070
Rick Miller       L  3910   2991  .599    919  .668    .069
Larry Parrish     R  6848   5075  .679   1773  .747    .068
Carlos Beltran    B  3508   2587  .748    921  .815    .068
Tony Taylor       R  6587   5304  .597   1283  .663    .067
Scott Fletcher    R  5294   4014  .583   1280  .649    .066
Johnny Edwards    L  4585   3471  .575   1114  .638    .063
Brent Mayne       L  3652   2701  .590    951  .649    .059
Troy O'Leary      L  4043   2917  .700   1126  .758    .058
Miguel Tejada     R  4277   3115  .726   1162  .782    .057
Orlando Merced    B  4028   2883  .682   1145  .738    .056
Henry Rodriguez   L  3054   2243  .719    811  .776    .056
Edgardo Alfonzo   R  4981   3700  .702   1281  .758    .056
 
Where: DIFF - BPS with runners in scoring position minus BPS without.

Just who I expected to see: Bill Spiers, Wayne Garrett, Rennie Stennett, Rick Miller....

And the other side of the coin:

                           - NO-RISP -  --- RISP --
Name              B    AB     AB   BPS     AB   BPS    DIFF
Richard Hidalgo   R  3193   2252  .821    941  .614   -.207
Jermaine Dye      R  3863   2750  .772   1113  .611   -.161
Al Martin         L  4269   3233  .753   1036  .598   -.155
Larry Brown       R  3472   2729  .574    743  .423   -.151
Earl Williams     R  3058   2186  .701    872  .554   -.147
Hal Morris        L  4037   2952  .769   1085  .624   -.145
Jim Edmonds       L  5139   3739  .868   1400  .727   -.141
Jim Morrison      R  3414   2494  .708    920  .570   -.139
Dean Palmer       R  4953   3586  .753   1367  .617   -.137
Pete Ward         L  3088   2253  .690    835  .553   -.136
Lee Maye          L  3849   2978  .708    871  .577   -.130
Mark Kotsay       L  3756   2898  .734    858  .611   -.123
Don Slaught       R  4101   3020  .721   1081  .599   -.122
Pat Borders       R  3183   2364  .662    819  .541   -.121
Todd Walker       L  3704   2772  .749    932  .630   -.119
Reggie Smith      B  7119   5306  .797   1813  .680   -.117
Shawn Green       L  5566   4102  .814   1464  .700   -.114
Tony Bernazard    B  3735   2808  .671    927  .558   -.114
Kevin Young       R  3944   2814  .721   1130  .608   -.113
Phil Bradley      R  3716   2836  .730    880  .618   -.112
Warren Cromartie  L  3958   3022  .705    936  .593   -.112
Lee Lacy          R  4582   3502  .718   1080  .606   -.112

For lack of a better term (and so I don't have to keep writing "the difference between a batter's BPS with and without runners in scoring position"), I'm going to call this difference ("DIFF" in the charts above) Clutch Percentage. I know it's not really "Clutch" and not really a "Percentage", but it's the best I could come up with.

The poor Clutch Percentages are more extreme than the positive ones, partly because the median of the group is not zero, but rather -.027.1

I'm not sure what I expected to see here. I doubt that if I had presented these two lists of players to you and told you that one was a list of the best clutch hitters and the other the worst, you could have figured out which was which.

One of the things that bothers me about the last list is that 12 of the 20 players on it have less than 1000 at-bats with runners in scoring position. Of the 727 players in the study, only a little more than 30% (222) fell into that category. If the differences we're looking at were caused more by chance than talent, you'd expect to see players with small sample sizes at the two extremes.

The raw data for the players in the study is here.

Is The Data Random?

Could these results have been random? The way I usually approach this kind of question is with brute force. Rather than attempting to finesse the issue with mathematics, I run over it with simulation. My approach this time is perhaps best shown by example.

In the games we have, Vada Pinson had a runner on second or third in 2114 of his 8954 at-bats, or 23.6096%. So to simulate his random career, I generated 8954 random numbers (one for each at-bat) between 0 and 1. If the number was less than .236096, I counted it as an at-bat with runners in scoring position. When I was done, I had randomly selected around 2114 at-bats that I'm considering to be clutch. Using these two pools of at-bats (the ones selected by this process and the ones not selected), I computed his simulated Clutch Percentage.

One problem with this approach is that we already know that the data is not random. Players on average hit worse (in terms of BPS, 27 points worse) with men in scoring position. Our random tests will not reflect this. Since we're doing these simulations to see how much random variation there will be in the data, this problem might not be fatal, but it does complicate things. For example, we will want to compare the amount of spread in both the real and simulated data. This spread will be centered around -.027 in the real run and .000 in the simulated runs.

I did 1000 of these simulations. What did I find out? Well, there was nothing terribly unusual in the spread of the real data. In the random run the average distance from each player's Clutch Percentage and the expected value varied from a low of .2872 to a high of .3512. The actual values differed by .3314, which was a little high but nothing out of the ordinary (117th place out of 1001). In addition to looking at the spread, I also broke the range of values into 20 groups (each .015 wide except for the first and last) and saw if the distribution of the players were similar in both the real and the simulated worlds. Note that the mid-point in the two worlds is different, since the expected Clutch Percentage is -.027 for the actual values and .000 for the simulated ones. In the chart below, then, group A contains the count of players from .000 to .015 over the expected value, B contains the count of players from .015 to .030, and so on. Not too surprisingly, -A contains the count of players from .000 to .015 below the expected value, -B contains the count of players from .015 to .030 below, and so on.

       -J  -I  -H  -G  -F  -E  -D  -C  -B  -A   A   B   C   D   E   F   G   H   I   J
Real    1   3   6   5  15  21  46  76  77 115 105  88  69  41  31  13  10   1   3   1
Fake    1   2   3   7  14  26  45  70  94 109 109  92  68  45  26  14   7   3   2   1

Our real distribution is very similar to the average of the fake ones. But it is important to note that this doesn't prove anything. While a very different spread and distribution could be used to demonstrate that Clutch Percentage is not random, the fact that these results are similar is not evidence that only random forces are at work here.

Potential Problems

This section will explore some factors that might complicate things, causing batters to hit worse (or better) with runners in scoring position.

The first thing that occurred to me is that batters might be facing a platoon disadvantage more often with runners in scoring position than they might otherwise. To test this, I looked at batters who hit right, left and from both sides of the plate and determined how well they did against right and left pitchers. I next computed what types of pitchers they faced both with and without runners in scoring position and used that information to generate an expected BPS (batting average plus slugging percentage) given the mix of pitchers they saw in both situations. Here's the data:

                                       - NO RISP -  --- RISP --
       Count    AB   BPS  BPSvR BPSvL  PLAT% ExBPS  PLAT% ExBPS
Right    402  5247  .679   .662  .714   36.8  .679   34.1  .677
Left     219  5298  .693   .718  .612   75.9  .694   71.8  .689
Both     106  5220  .652   .651  .647   50.9  .652   50.0  .651
Total    727  5259  .679   .677  .6732  50.6  .679   47.8  .677
 
Where: Count - the number of batter included in sample
       AB    - the average number of at-bats in group
       BPS   - the average overall BPS
       BPSvR - the average BPS against right-handed pitchers
       BPSvL - the average BPS against left-handed pitchers
       PLAT% - the percentage of times having the platoon advantage
       ExBPS - the expected BPS given the mix of pitchers faced

This table presents a lot of unfamiliar information so it might be a good idea to go over a sample line. There are 402 right-handed hitters in our study. The average righty in the study had 5247 at-bats and an overall BPS of .679. As expected, he hit lefties better than the righties (.714 to .662), but had a platoon advantage only 36.8% of the time with no runners in scoring position. Now, I didn't assume that all right-handed hitters had a platoon advantage against left-handed pitchers. Instead, I determined which type of pitcher each batter performed better against over the course of his career. Most of the time, hitters did better against pitchers who threw from the other side, but not always. Given the percentage of pitchers of each type our hitters faced with no one in scoring position, and how they hit against these pitchers, righty hitters had an expected BPS of .679 in these situations. When runners were on second or third, the platoon advantage and BPS drop slightly to 34.1% and .677 respectively.

You should not assume from the chart above that switch-hitters had no platoon advantage or disadvantage. The reason why they hit almost the same against both righties (.651) and lefties (.647) is that the platoon differentials of switch-hitters tended to cancel each other out. To illustrate this, here are the players with 3000 or more at-bats with the greatest platoon differentials:

Name              B    P BPSvR BPSvL  Diff
Rob Deer          R vs L  .599  .801  .202
Adrian Beltre     R vs R  .750  .667  .083
Tom Goodwin       L vs L  .599  .621  .021
Randy Bush        L vs R  .666  .369  .298
Dave Hollins      B vs L  .608  .806  .198
Wally Backman     B vs R  .652  .365  .287
 
Where: B     - the handedness of the batter (R - right, L- left, B- both)
       P     - the handedness of the pitcher (R - right, L- left)
       BPSvR - the batting average plus slugging percentage against right-handed pitchers
       BPSvL - the batting average plus slugging percentage against left-handed pitchers
       Diff  - the difference

The average platoon differential is greatest for the lefties in our study (.107), and just about the same for right-handed hitters (.058) and switch-hitters (.057). People often assume that just because a batter hits from both sides of the plate that he hits equally well from each side. This is not the case, although it isn't always obvious which side is their weakest (unless it's someone like Wally Backman).

Platoon advantages by themselves are not sufficient to explain the fact that hitters tend to perform worse with runners in scoring position. The average dropoff is about 23 points of BPS (.693 to .670), and the expected dropoff due to platoon disadvantages is only 2 points for right-handed hitters, 5 points for lefties, and 1 point for switch-hitters. Of course, this effect is different for each player. Frank Howard, for example, punished lefties so much that he seldom faced them with men in scoring position, causing him to have a platoon disadvantage of 17 points. Tony Batista, on the other hand, is a right-handed hitter who has hit righties better than lefties over the course of his career. As a results, he has a platoon advantage of 3 points with runners in scoring position.

Another factor we might want to take into account is that the quality of pitchers is often worse in these situations. This makes sense. After all, when you're up with men in scoring position, you are usually facing the pitcher who permitted those runners to reach base, something that happens a lot more frequently with a Jaime Navarro on the mound than a Roger Clemens. To determine how much worse they are, for each at-bat by one of the batters in our study, I calculated the pitcher's opponents BPS, taking into account the handedness of the batter. I found that the the average pitcher when runners are in scoring position is about 3 to 4 points worse (in BPS) than those on the mound when there aren't. Not a big deal and a result that seems to balance out the platoon disadvantage, except, as with the platoon disadvantage, there are differences from player to player. The most extreme cases among the players in our study are Hal Morris, who has faced pitchers 17 points worse with runners in scoring position, and Larry Walker, who has faced pitchers 11 points better. All in all, I think it's a good thing to check before anointing someone either a great or a poor clutch hitter.

The last thing we want to look at is any possible park effects. After all, there are more runners in scoring position in good hitting parks. So I calculated the average park factor for the two situations and, to make a long story even longer, here's what I found:

                AB    PFact
NO RISP    2848124   1.0048
RISP        974948   1.0084
 
Where: PFact - the average park factor.

Since there are more at-bats in a typical game in a hitter's park then there are in a pitcher's park, it's not too surprising that the average park factor in both groups would be greater than 1. Note that the advantage with runners in scoring position is slight. Still, this is not insignificant for all players. The two extremes:

                NO-RISP  RISP
Name              PFact PFact  PRat
Lee Maye           .980  .970  .990
Todd Helton       1.214 1.259 1.037
 
Where: PRat - the RISP park factor divided by the NO-RISP park factor.

It is perhaps not too surprising that a member of the Colorado Rockies got the biggest park factor boost with runners in scoring position.

A Last Look At The Players

I wanted to take one last look at the players at the top and bottom of our lists, this time with their platoon, strength of opposition and park factors included.

                          - NO-RISP -  -- RISP --
Name              B    AB    AB   BPS    AB   BPS   DIFF   PlatF  OppF   PRat
Bill Spiers       L  3430  2548  .607   882  .722   .115   -.006  .003   .999
Mike Sweeney      R  3760  2673  .764  1087  .867   .103    .003  .013   .998
Pat Tabler        R  3948  2815  .626  1133  .725   .099   -.001  .008  1.005
Jose Valentin     B  4882  3678  .666  1204  .765   .099   -.001  .013  1.007
Wayne Garrett     L  3308  2557  .557   751  .643   .087   -.004  .008   .994
Sandy Alomar      B  4748  3831  .519   917  .592   .073   -.000  .005  1.008
Tony Fernandez    B  7972  6100  .665  1872  .736   .071    .000  .008  1.003
Rennie Stennett   R  4554  3520  .612  1034  .682   .070   -.004  .011  1.004
Joe Girardi       R  4150  3117  .596  1033  .666   .070    .001  .005  1.011
Rick Miller       L  3910  2991  .599   919  .668   .069   -.010  .012  1.002
Larry Parrish     R  6848  5075  .679  1773  .747   .068   -.001  .003  1.001
Carlos Beltran    B  3508  2587  .748   921  .815   .068   -.001  .006  1.008
Tony Taylor       R  6587  5304  .597  1283  .663   .067   -.001  .007  1.007
Scott Fletcher    R  5294  4014  .583  1280  .649   .066   -.003 -.005  1.001
Johnny Edwards    L  4585  3471  .575  1114  .638   .063   -.003  .010  1.004
Brent Mayne       L  3652  2701  .590   951  .649   .059   -.002  .012  1.024
Troy O'Leary      L  4043  2917  .700  1126  .758   .058   -.005 -.001  1.003
Miguel Tejada     R  4277  3115  .726  1162  .782   .057   -.000  .008  1.002
Orlando Merced    B  4028  2883  .682  1145  .738   .056   -.001  .009  1.006
Henry Rodriguez   L  3054  2243  .719   811  .776   .056   -.004  .003  1.007
Edgardo Alfonzo   R  4981  3700  .702  1281  .758   .056   -.000  .008  1.010
 
Where: PlatF - is the platoon advantage or disadvantage with runners in scoring position
       OppF  - is the expected BPS increase or decrease based upon the quality of pitchers

Some of these hitters (Mike Sweeney, Jose Valentin, Rennie Stennett and Johnny Edwards) got a bigger than average boost by facing weaker pitchers with runners in scoring position, and Brent Mayne had the advantage of both facing weaker than normal pitching and hitting in these situations in friendlier parks. My feeling is that Mayne would not have been on the list without this help.

The bottom list revisited:

                          - NO-RISP -  -- RISP --
Name              B    AB    AB   BPS    AB   BPS   DIFF   PlatF  OppF   PRat
Richard Hidalgo   R  3193  2252  .821   941  .614  -.207    .000  .006   .996
Jermaine Dye      R  3863  2750  .772  1113  .611  -.161   -.001  .005  1.003
Al Martin         L  4269  3233  .753  1036  .598  -.155   -.017  .002  1.011
Larry Brown       R  3472  2729  .574   743  .423  -.151   -.004  .005  1.001
Earl Williams     R  3058  2186  .701   872  .554  -.147    .000  .002  1.000
Hal Morris        L  4037  2952  .769  1085  .624  -.145   -.002  .017  1.006
Jim Edmonds       L  5139  3739  .868  1400  .727  -.141   -.008  .005  1.001
Jim Morrison      R  3414  2494  .708   920  .570  -.139   -.001  .004  1.000
Dean Palmer       R  4953  3586  .753  1367  .617  -.137   -.006 -.001  1.003
Pete Ward         L  3088  2253  .690   835  .553  -.136   -.002  .013  1.005
Lee Maye          L  3849  2978  .708   871  .577  -.130   -.006 -.005   .990
Mark Kotsay       L  3756  2898  .734   858  .611  -.123   -.001  .006  1.006
Don Slaught       R  4101  3020  .721  1081  .599  -.122   -.003 -.004   .995
Pat Borders       R  3183  2364  .662   819  .541  -.121   -.001  .005   .999
Todd Walker       L  3704  2772  .749   932  .630  -.119   -.007  .007  1.011
Reggie Smith      B  7119  5306  .797  1813  .680  -.117    .001  .001  1.006
Shawn Green       L  5566  4102  .814  1464  .700  -.114   -.006 -.001  1.006
Tony Bernazard    B  3735  2808  .671   927  .558  -.114    .000  .005  1.006
Kevin Young       R  3944  2814  .721  1130  .608  -.113   -.002  .008  1.000
Phil Bradley      R  3716  2836  .730   880  .618  -.112   -.003  .000  1.005
Warren Cromartie  L  3958  3022  .705   936  .593  -.112   -.004  .001  1.001
Lee Lacy          R  4582  3502  .718  1080  .606  -.112   -.003 -.001  1.001

It looks like only Al Martin (with a bad platoon factor) and Lee May (who seemed to have everything go against him in these situations) could claim to owe their spots on this list to forces beyond their control. Hal Morris, on the other hand, had reasonably good factors and still hit poorly with runners in scoring position.

The data for all the players in the study is here.

Conclusion

So did I find evidence of clutch hitting? Not really. I did come up with lists of players who performed well and poorly in this area. Along the way, I presented quite a bit of data on situational hitting, platoon advantages, opposition pitching strength and park effects, and I attempted to both understand and explain what I found. At the end of all this, however, I guess I'm still not convinced that the players owe their inclusion on these lists of mine to talent rather than luck. Even when dealing with sample sizes of several thousand at-bats, the amount of random variation that I found in my simulations was very close to what I found in the real data. As I mentioned before, this doesn't necessarily mean that there isn't some real differences buried in all that noise, only that I'm not sure I found them. One could argue that the forces at work here, if they exist, must be awfully weak to so closely mimic random noise, and if they are really that inconsequential perhaps we could assume they don't exist without much loss of accuracy.

Notes

Note 1:

Earlier I had mentioned the players as a whole hit an average of 23 points worse with men in scoring position. But the average Clutch Percentage is -27 points. This might seem confusing but hopefully won't after an example.

Let's say their are three players: Moe, Larry and Curly. Here are the performance both with and without men in scoring position:

           - NO RISP -  -- RISP --
Name         AB    BPS    AB    BPS     CLP
Moe          50   .600    20   .200   -.400
Larry       150   .600    30   .500   -.100
Curly       200   .700    50   .800    .100
Total       400   .650   100   .590   -.060

So as a group, they hit 60 points worse with men in scoring position, but this counts Curly's contribution much more heavily than Moe's. If we average their respective Clutch Percentages (and so count each player equally), we get .633 in the "NO RISP" group ((.600+.600+.700) / 3), .500 in the "RISP" group ((.200+.500+.800) / 3), and an average difference of 133 points.

Note 2:

Some of these averages look weird. For example, the overall BPS for switch-hitters (.652) is greater than the players' averages against BOTH righties (.651) and lefties (.647). This would seem, on the face of it, to be a mathematical impossibility. It is caused by the manner in which I determined the averages, and is best shown by example. Let's say we have two players, Moe and Larry, who have the following right-left splits:

           -- VS R --  -- VS L --   -- TOT  --
Name        AB    BPS   AB    BPS    AB    BPS
Moe         10   .200   90   .600   100   .560
Larry       90   .600   10   .200   100   .560
Total            .400        .400         .560

So when I average these, I do not weight them by at-bats, which would cause the players with more at-bats to influence the results more than someone just over the 3000 at-bat minimum, but just take an average of the averages. And since players tend to have fewer plate appearances when they do not have the platoon advantage (as I showed in an extreme example above), the results can look a little strange at times.