Kiepura 1999

Performance0-Rank  0-Score1-Rank  1-Score2-Rank  2-Score3-Rank  3-Score3R-Rank  3R-Score4-Rank  4-Score  NED
Ashkenazy 1981   25  0.8635  0.0032  0.0543  0.0548  0.0647  0.05
Bacha 1997   36  0.8541  0.0045  0.0448  0.0445  0.0641  0.05
Barbosa 1983   1  0.935  0.066  0.293  0.5923  0.165  0.31
Biret 1990   35  0.858  0.0240  0.0546  0.0534  0.0931  0.07
Block 1995   40  0.8324  0.0026  0.0727  0.0721  0.1523  0.10
Brailowsky 1960   2  0.927  0.028  0.266  0.5326  0.137  0.26
Chiu 1999   22  0.8737  0.0030  0.0541  0.0533  0.0735  0.06
Clidat 1994   9  0.9126  0.0012  0.108  0.4136  0.0612  0.16
Cohen 1997   38  0.8434  0.0037  0.0728  0.0741  0.0638  0.06
Cortot 1951   34  0.8525  0.0046  0.0540  0.0548  0.0548  0.05
Csalog 1996   24  0.8746  0.0042  0.0630  0.0636  0.0933  0.07
Czerny 1990   8  0.914  0.103  0.225  0.5532  0.098  0.22
Ezaki 2006   11  0.9013  0.019  0.197  0.4846  0.0516  0.15
Ferenczy 1958   19  0.8721  0.0023  0.0923  0.1648  0.0526  0.09
Fliere 1977   32  0.8628  0.0033  0.0638  0.0644  0.0543  0.05
Fou 1978   6  0.9110  0.025  0.232  0.6010  0.471  0.53
Francois 1956   20  0.8743  0.0039  0.0634  0.0644  0.0640  0.06
Grinberg 1951   39  0.8447  0.0041  0.0539  0.0545  0.0645  0.05
Hatto 1993   30  0.8627  0.0024  0.0824  0.1446  0.0529  0.08
Hatto 1997   31  0.8642  0.0028  0.0729  0.0746  0.0534  0.06
Indjic 2001   29  0.8616  0.0127  0.0633  0.0646  0.0546  0.05
Jonas 1947   10  0.9032  0.0016  0.1218  0.2638  0.0820  0.14
Kapell 1951   17  0.8714  0.0111  0.1120  0.1747  0.0625  0.10
Kiepura 1999   target  targettarget  targettarget  targettarget  targettarget  targettarget  target
Kushner 1989   43  0.8220  0.0048  0.0544  0.0540  0.0737  0.06
Luisada 1991   28  0.8651  0.0031  0.0636  0.0647  0.0542  0.05
Lushtak 2004   21  0.8748  0.0022  0.0822  0.1648  0.0427  0.08
Magaloff 1978   41  0.8322  0.0038  0.0631  0.0631  0.0636  0.06
Meguri 1997   16  0.8844  0.0029  0.0635  0.0647  0.0550  0.05
Milkina 1970   44  0.8118  0.0144  0.0449  0.0445  0.0551  0.04
Mohovich 1999   37  0.856  0.0313  0.1021  0.1645  0.0624  0.10
Niedzielski 1931   18  0.8715  0.0121  0.0919  0.1936  0.0722  0.12
Ohlsson 1999   45  0.8152  0.0047  0.0637  0.0633  0.0739  0.06
Olejniczak 1990   7  0.9112  0.0114  0.0913  0.3339  0.0815  0.16
Osinska 1989   26  0.8640  0.0018  0.0812  0.3420  0.236  0.28
Rangell 2001   46  0.7730  0.0049  0.0726  0.0739  0.0730  0.07
Richter 1976   27  0.8611  0.0110  0.1310  0.3733  0.0810  0.17
Rubinstein 1938   47  0.7749  0.0025  0.0725  0.1448  0.0432  0.07
Rubinstein 1952   15  0.883  0.164  0.189  0.3810  0.493  0.43
Rubinstein 1961   48  0.7450  0.0034  0.0545  0.0517  0.4417  0.15
Rubinstein 1966   49  0.7438  0.0035  0.0542  0.0520  0.4418  0.15
Shebanova 2002   4  0.921  0.231  0.231  0.6118  0.302  0.43
Smidowicz 1948   14  0.8919  0.0020  0.1515  0.3137  0.0814  0.16
Smidowicz 1948b   13  0.8917  0.0119  0.0914  0.3135  0.0813  0.16
Smith 1975   42  0.8231  0.0036  0.0632  0.0614  0.509  0.17
Sofronitsky 1949   23  0.872  0.222  0.2211  0.3748  0.0519  0.14
Sztompka 1959   3  0.929  0.027  0.194  0.5618  0.304  0.41
Tomsic 1995   5  0.9136  0.0015  0.1416  0.3144  0.0621  0.14
Uninsky 1971   33  0.8633  0.0043  0.0447  0.0439  0.0649  0.05
Wasowski 1980   12  0.9039  0.0017  0.0917  0.2829  0.0911  0.16
Random 1   50  0.0223  0.0050  0.0251  0.0239  0.0452  0.03
Random 2   52  -0.1045  0.0052  0.0252  0.0221  0.1344  0.05
Random 3   51  0.0129  0.0051  0.0350  0.0322  0.2028  0.08

Note: To load data table give above into Excel, copy and paste the data into a text editor (such as WordPad) first, then copy the text in the editor and past into Excel. You should remove the "target" line from the data before pasting into Excel so that plotting graphs of the data is done properly.

Column descriptions

  • Performance:
  • 0-Rank/0-Score: 0-Score is equivalent to Pearson correlation of the entire data sequence between the reference performance and a test performance. 0-Rank is the sorting order of the 0-scores (highest score has a rank of 1).
  • 1-Rank/1-Score: 1-Score is the area fraction covered by a particular performance in the scape plot (see image above). These values should not be taken literally, since they are sensitive to the Hatto Effect.
  • 2-Rank/2-Score: 2-Score values are equivalent to 1-Score values with all higher-ranking performances removed before the calculation of the area of coverage in the scape is calculated. Improvment over the 1-Rank scores, but still somewhat sensitive to the Hatto Effect.
  • 3-Rank/3-Score: Similar to 2-Rank calculations. The bottom 1/2 of the 2-rank performances are kept constant as a noise floor for the similarity measurement. Then one-by-one the top 1/2 of the 2-rank performances are superimposed with the noise-floor performances, and a 3-score is measured as the area covered in the scape. This measure is not sentisive to the Hatto Effect.
  • 3R-Rank/3R-Score: Reverse 3-rank/3-scores. 3-rankings and scores are not symmetric (A->B values are different from B->A values). So this column represents similarity measures in the opposite direction.
  • 4-Rank/4-Score: The geometric mean between 3-scores and 3R-scores. This column gives the best overall similarity ranking between the various performances (see color codes below).
  • NED: Noise Equivalient Distance (not yet implemented)

Color codes for 3-rank listings:

  • red = strongly similar performance to target
  • orange = moderately similar performance
  • yellow = weakly similar performance
  • green = marginally similar/dissimilar performance
  • white = dissimilar to target
  • blue = false positive (has high 3-rank score but low 3R-rank score)

3-rank/scores are not symmetric, so the 3R-rank/score columns give the 3-rank/scores going in the opposite direction. More matches in the 3-rank column than in the 3R-rank column indicates an individualistic performance, while more matches in the 3R-rank column indicates a mainstream performance.

If a 3-rank and a 3R-rank are both marked as similar to each other, then there is a possible direct relation between the performances. If one is similar to the other but not in the reverse direction, then the similarity is more likely to be by chance (performers randomly chose a similar interpretation).