Distributions

League-wide player histograms · 15 games played · 231 qualifying batters (3+ PA) · 134 qualifying pitchers (3+ outs)
JSON
Players Teams
Clear
Player ID 988 not found in the qualifying population — no histogram marker shown.

Batting distributions

PAVG n=231 · μ=0.387 · σ=0.241
0.000
34
0.083
0
0.167
19
0.250
27
0.333
58
0.417
2
0.500
32
0.583
11
0.667
26
0.750
17
0.833
2
0.917
3
10th25th Median 75th90th 95th99th
0.000 0.250 0.333 0.600 0.714 0.750 0.950
OPS n=231 · μ=1.183 · σ=0.664
0.000
25
0.312
23
0.625
31
0.938
38
1.250
44
1.562
39
1.875
15
2.188
8
2.500
7
2.812
0
3.125
0
3.438
1
10th25th Median 75th90th 95th99th
0.250 0.667 1.200 1.667 2.000 2.333 2.647
OPS+ n=231 · μ=99 · σ=55.498
0
25
25
20
50
34
75
42
100
34
125
37
150
17
175
12
200
7
225
2
250
0
275
1
10th25th Median 75th90th 95th99th
22 59 100 134 174 197 226
wOBA n=231 · μ=0.532 · σ=0.277
0.000
12
0.107
22
0.213
17
0.320
29
0.427
33
0.533
36
0.640
24
0.746
25
0.853
18
0.960
9
1.066
5
1.173
1
10th25th Median 75th90th 95th99th
0.137 0.372 0.566 0.737 0.873 0.983 1.159
BAVG n=231 · μ=0.467 · σ=0.290
0.000
34
0.125
7
0.250
52
0.375
15
0.500
51
0.625
30
0.750
21
0.875
0
1.000
20
1.125
0
1.250
0
1.375
1
10th25th Median 75th90th 95th99th
0.000 0.333 0.500 0.667 0.800 1.000 1.000
ISO n=231 · μ=0.257 · σ=0.309
0.000
106
0.146
37
0.292
34
0.438
19
0.583
15
0.729
9
0.875
9
1.021
0
1.167
0
1.312
0
1.458
1
1.604
1
10th25th Median 75th90th 95th99th
0.000 0.000 0.200 0.400 0.667 0.833 1.000
BABIP n=231 · μ=0.575 · σ=0.367
0.000
39
0.167
5
0.333
21
0.500
66
0.667
40
0.833
2
1.000
52
1.167
1
1.333
0
1.500
4
1.667
0
1.833
1
10th25th Median 75th90th 95th99th
0.000 0.333 0.500 0.929 1.000 1.000 1.500
K% n=231 · μ=20.6% · σ=19.4%
0.0%
86
6.2%
0
12.5%
17
18.8%
22
25.0%
32
31.2%
32
37.5%
13
43.8%
0
50.0%
17
56.2%
3
62.5%
8
68.8%
1
10th25th Median 75th90th 95th99th
0.0% 0.0% 20.0% 33.3% 50.0% 55.0% 66.7%
BB% n=231 · μ=13.1% · σ=15.8%
0.0%
123
6.2%
0
12.5%
16
18.8%
19
25.0%
33
31.2%
27
37.5%
6
43.8%
0
50.0%
3
56.2%
2
62.5%
1
68.8%
1
10th25th Median 75th90th 95th99th
0.0% 0.0% 0.0% 25.0% 33.3% 40.0% 60.0%
2C-RBI% n=231 · μ=2.9% · σ=14.4%
0.0%
221
8.3%
0
16.7%
0
25.0%
0
33.3%
1
41.7%
0
50.0%
4
58.3%
0
66.7%
2
75.0%
0
83.3%
0
91.7%
3
10th25th Median 75th90th 95th99th
0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 90.0%
2C-Conv% n=231 · μ=6.5% · σ=24.6%
0.0%
216
8.3%
0
16.7%
0
25.0%
0
33.3%
0
41.7%
0
50.0%
0
58.3%
0
66.7%
0
75.0%
0
83.3%
0
91.7%
15
10th25th Median 75th90th 95th99th
0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 100.0%
MhAB% n=231 · μ=0.8% · σ=5.1%
0.0%
225
4.2%
0
8.3%
0
12.5%
0
16.7%
1
20.8%
0
25.0%
2
29.2%
0
33.3%
2
37.5%
0
41.7%
0
45.8%
1
10th25th Median 75th90th 95th99th
0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 30.8%
RISP-AVG n=231 · μ=0.376 · σ=0.312
0.000
60
0.167
28
0.333
49
0.500
46
0.667
30
0.833
2
1.000
14
1.167
1
1.333
0
1.500
0
1.667
0
1.833
1
10th25th Median 75th90th 95th99th
0.000 0.000 0.333 0.500 0.750 1.000 1.000
RISP-OPS n=231 · μ=1.146 · σ=0.839
0.000
47
0.417
41
0.833
41
1.250
48
1.667
32
2.083
8
2.500
8
2.917
4
3.333
0
3.750
0
4.167
0
4.583
2
10th25th Median 75th90th 95th99th
0.000 0.500 1.000 1.667 2.000 2.500 3.000
RISP-Conv n=231 · μ=0.47 · σ=0.461
0.00
77
0.25
48
0.50
44
0.75
20
1.00
28
1.25
5
1.50
6
1.75
0
2.00
2
2.25
0
2.50
0
2.75
1
10th25th Median 75th90th 95th99th
0.00 0.00 0.33 0.75 1.00 1.29 1.90
WAR n=231 · μ=0.01 · σ=0.084
-0.25
2
-0.21
3
-0.16
5
-0.12
20
-0.08
24
-0.04
55
-0.00
42
0.04
36
0.08
19
0.12
16
0.16
7
0.20
2
10th25th Median 75th90th 95th99th
-0.10 -0.04 0.01 0.07 0.13 0.15 0.19

Pitching distributions

wERA n=134 · μ=16.36 · σ=10.874
0.00
7
4.20
29
8.41
28
12.61
19
16.81
16
21.02
12
25.22
4
29.43
4
33.63
8
37.83
2
42.04
3
46.24
1
10th25th Median 75th90th 95th99th
5.70 7.99 13.54 21.14 35.15 38.30 46.34
xRA n=134 · μ=16.36 · σ=10.874
0.00
7
4.20
29
8.41
28
12.61
19
16.81
16
21.02
12
25.22
4
29.43
4
33.63
8
37.83
2
42.04
3
46.24
1
10th25th Median 75th90th 95th99th
5.70 7.99 13.54 21.14 35.15 38.30 46.34
GSc avg n=134 · μ=52.3 · σ=6.913
36.3
2
38.8
8
41.4
12
44.0
10
46.6
9
49.2
19
51.8
22
54.4
11
57.0
17
59.5
16
62.1
5
64.7
3
10th25th Median 75th90th 95th99th
42.7 47.9 52.5 58.1 61.0 62.2 65.8
GSc+ n=134 · μ=103 · σ=13.877
70
2
75
4
80
12
86
11
91
9
97
22
102
22
107
14
113
16
118
10
124
9
129
3
10th25th Median 75th90th 95th99th
84 93 103 115 121 126 129
FOP n=134 · μ=61 · σ=14.530
15
2
21
1
26
6
32
7
37
5
43
5
48
5
54
17
59
21
65
30
70
21
76
14
10th25th Median 75th90th 95th99th
37 55 64 71 76 78 81
OS+ n=134 · μ=126 · σ=37.065
78
29
91
0
104
31
117
0
130
27
142
0
155
30
168
0
181
12
194
0
207
4
220
1
10th25th Median 75th90th 95th99th
78 104 130 155 181 181 207
GE n=134 · μ=0.2 · σ=0.053
0.1
29
0.1
0
0.1
31
0.2
0
0.2
27
0.2
0
0.2
30
0.2
0
0.3
12
0.3
0
0.3
4
0.3
1
10th25th Median 75th90th 95th99th
0.1 0.1 0.2 0.2 0.3 0.3 0.3
K% n=134 · μ=32.2% · σ=17.9%
0.0%
12
6.2%
0
12.5%
22
18.8%
3
25.0%
33
31.2%
8
37.5%
26
43.8%
4
50.0%
10
56.2%
10
62.5%
1
68.8%
5
10th25th Median 75th90th 95th99th
12.5% 17.5% 28.6% 42.9% 57.1% 60.0% 71.4%
BB% n=134 · μ=12.2% · σ=11.0%
0.0%
49
3.1%
0
6.2%
0
9.4%
6
12.5%
42
15.6%
3
18.8%
3
21.9%
5
25.0%
7
28.1%
13
31.2%
3
34.4%
3
10th25th Median 75th90th 95th99th
0.0% 0.0% 14.3% 19.2% 28.6% 28.6% 37.5%
HR% n=134 · μ=2.8% · σ=6.4%
0.0%
111
2.4%
0
4.8%
0
7.1%
0
9.5%
1
11.9%
1
14.3%
14
16.7%
3
19.0%
1
21.4%
1
23.8%
0
26.2%
2
10th25th Median 75th90th 95th99th
0.0% 0.0% 0.0% 0.0% 14.3% 15.1% 26.5%
K-BB% n=134 · μ=11.5% · σ=21.0%
-28.6%
9
-21.4%
2
-14.3%
17
-7.1%
0
0.0%
29
7.1%
8
14.3%
24
21.4%
9
28.6%
17
35.7%
6
42.9%
6
50.0%
7
10th25th Median 75th90th 95th99th
-14.3% 0.0% 14.3% 28.6% 40.0% 46.4% 57.1%
oAVG n=134 · μ=0.396 · σ=0.193
0.000
9
0.083
6
0.167
9
0.250
20
0.333
25
0.417
13
0.500
30
0.583
8
0.667
11
0.750
1
0.833
1
0.917
1
10th25th Median 75th90th 95th99th
0.143 0.286 0.400 0.500 0.654 0.667 0.838
WAR n=134 · μ=0.06 · σ=0.085
-0.14
4
-0.10
9
-0.07
9
-0.03
10
0.00
21
0.04
21
0.07
18
0.10
22
0.14
9
0.17
7
0.21
2
0.24
2
10th25th Median 75th90th 95th99th
-0.07 0.00 0.06 0.12 0.16 0.18 0.25
Methodology
Each histogram is a 12-bucket equal-width binning of the qualifying-player population's values. Bucket bounds run from min to max across the population — no fixed domain. The labels on the left are the bucket's lower bound; the count on the right is how many players fell into it. Bar widths are normalized to the largest-count bucket (so the longest bar = 100% of the panel width).

Qualifying thresholds are the same as on the Leaders page — ~1× games-per-team in PA for batters and outs for pitchers, scaled with games played.

Percentiles use linear interpolation (matches numpy percentile() default). σ is population standard deviation across the qualifying group.

The Decay distribution is restricted to pitchers with cross-arc sample (faced batters in both arc-1 and arc-3), so the n on that panel is lower than the rest of the pitching panels. Pitchers without enough sample to compute Decay aren't binned.

xRA is the non-negative linear-weights expected runs allowed (HR=1.4, single≈0.45, BB/HBP=0.32, K/out=0), multiplicatively anchored so league xRA matches league xRA. K-BB% is plain (K - BB) / BF.
Compare basket · 0 max 4
Compare →