Distributions

League-wide player histograms · 15 games played · 215 qualifying batters (3+ PA) · 133 qualifying pitchers (3+ outs)
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Player ID 5476 not found in the qualifying population — no histogram marker shown.

Batting distributions

PAVG n=215 · μ=0.391 · σ=0.244
0.000
34
0.083
0
0.167
10
0.250
25
0.333
55
0.417
1
0.500
40
0.583
9
0.667
23
0.750
12
0.833
1
0.917
5
10th25th Median 75th90th 95th99th
0.000 0.250 0.333 0.500 0.695 0.800 1.000
OPS n=215 · μ=1.178 · σ=0.644
0.000
11
0.250
13
0.500
36
0.750
25
1.000
34
1.250
21
1.500
32
1.750
13
2.000
16
2.250
4
2.500
6
2.750
4
10th25th Median 75th90th 95th99th
0.360 0.667 1.000 1.667 2.000 2.413 2.750
OPS+ n=215 · μ=97 · σ=52.540
0
12
22
15
44
45
67
23
89
36
111
27
133
29
155
9
178
12
200
3
222
3
244
1
10th25th Median 75th90th 95th99th
31 59 89 133 166 189 230
wOBA n=215 · μ=0.606 · σ=0.385
0.000
11
0.142
40
0.284
38
0.426
20
0.568
28
0.710
29
0.853
15
0.995
11
1.137
7
1.279
4
1.421
7
1.563
5
10th25th Median 75th90th 95th99th
0.184 0.294 0.551 0.836 1.141 1.430 1.652
BAVG n=215 · μ=0.456 · σ=0.278
0.000
34
0.083
0
0.167
3
0.250
14
0.333
50
0.417
1
0.500
35
0.583
13
0.667
30
0.750
18
0.833
3
0.917
14
10th25th Median 75th90th 95th99th
0.000 0.333 0.500 0.667 0.800 1.000 1.000
ISO n=215 · μ=0.234 · σ=0.300
0.000
103
0.111
24
0.222
27
0.333
20
0.444
10
0.556
8
0.667
7
0.778
2
0.889
1
1.000
11
1.111
1
1.222
1
10th25th Median 75th90th 95th99th
0.000 0.000 0.167 0.333 0.667 1.000 1.000
BABIP n=215 · μ=0.578 · σ=0.369
0.000
40
0.167
6
0.333
14
0.500
49
0.667
49
0.833
0
1.000
54
1.167
0
1.333
0
1.500
2
1.667
0
1.833
1
10th25th Median 75th90th 95th99th
0.000 0.333 0.600 1.000 1.000 1.000 1.430
K% n=215 · μ=20.6% · σ=19.0%
0.0%
76
6.7%
3
13.3%
22
20.0%
41
26.7%
3
33.3%
33
40.0%
12
46.7%
16
53.3%
0
60.0%
1
66.7%
7
73.3%
1
10th25th Median 75th90th 95th99th
0.0% 0.0% 20.0% 33.3% 50.0% 50.0% 66.7%
BB% n=215 · μ=14.2% · σ=16.4%
0.0%
103
5.6%
0
11.1%
7
16.7%
40
22.2%
27
27.8%
1
33.3%
24
38.9%
4
44.4%
0
50.0%
2
55.6%
1
61.1%
6
10th25th Median 75th90th 95th99th
0.0% 0.0% 14.3% 25.0% 33.3% 40.9% 66.7%
2C-RBI% n=215 · μ=4.1% · σ=15.6%
0.0%
198
8.3%
1
16.7%
1
25.0%
1
33.3%
4
41.7%
0
50.0%
6
58.3%
0
66.7%
1
75.0%
0
83.3%
0
91.7%
3
10th25th Median 75th90th 95th99th
0.0% 0.0% 0.0% 0.0% 0.0% 40.0% 95.3%
2C-Conv% n=215 · μ=8.4% · σ=27.7%
0.0%
197
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%
18
10th25th Median 75th90th 95th99th
0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 100.0%
MhAB% n=215 · μ=0.9% · σ=4.5%
0.0%
206
2.8%
0
5.6%
0
8.3%
0
11.1%
0
13.9%
1
16.7%
1
19.4%
4
22.2%
0
25.0%
2
27.8%
0
30.6%
1
10th25th Median 75th90th 95th99th
0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 24.3%
RISP-AVG n=215 · μ=0.380 · σ=0.322
0.000
61
0.167
24
0.333
33
0.500
54
0.667
26
0.833
0
1.000
16
1.167
0
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=215 · μ=1.135 · σ=0.850
0.000
43
0.417
45
0.833
39
1.250
38
1.667
27
2.083
9
2.500
6
2.917
4
3.333
2
3.750
1
4.167
0
4.583
1
10th25th Median 75th90th 95th99th
0.000 0.500 1.000 1.550 2.143 2.550 3.333
RISP-Conv n=215 · μ=0.45 · σ=0.437
0.00
67
0.17
19
0.33
25
0.50
39
0.67
29
0.83
2
1.00
22
1.17
1
1.33
1
1.50
5
1.67
3
1.83
2
10th25th Median 75th90th 95th99th
0.00 0.00 0.40 0.71 1.00 1.27 1.67
WAR n=215 · μ=0.02 · σ=0.085
-0.19
5
-0.15
3
-0.12
17
-0.08
22
-0.04
45
-0.01
29
0.03
26
0.07
29
0.11
23
0.14
9
0.18
4
0.22
3
10th25th Median 75th90th 95th99th
-0.08 -0.04 0.02 0.08 0.13 0.15 0.23

Pitching distributions

wERA n=133 · μ=15.75 · σ=9.962
0.00
9
3.94
20
7.87
32
11.81
13
15.75
18
19.68
11
23.62
8
27.56
12
31.49
4
35.43
4
39.36
1
43.30
1
10th25th Median 75th90th 95th99th
4.24 8.16 12.85 22.49 29.92 33.26 40.84
xRA n=133 · μ=15.75 · σ=9.962
0.00
9
3.94
20
7.87
32
11.81
13
15.75
18
19.68
11
23.62
8
27.56
12
31.49
4
35.43
4
39.36
1
43.30
1
10th25th Median 75th90th 95th99th
4.24 8.16 12.85 22.49 29.92 33.26 40.84
GSc avg n=133 · μ=45.1 · σ=5.961
30.9
4
33.2
4
35.4
8
37.7
8
40.0
18
42.2
25
44.5
15
46.8
14
49.0
11
51.3
16
53.6
8
55.8
2
10th25th Median 75th90th 95th99th
36.8 41.7 44.3 49.9 53.0 54.6 56.3
GSc+ n=133 · μ=107 · σ=14.056
73
4
79
4
84
8
89
8
95
15
100
27
105
16
110
14
116
9
121
17
126
8
131
3
10th25th Median 75th90th 95th99th
87 99 105 118 126 129 133
FOP n=133 · μ=59 · σ=11.883
16
1
22
0
27
5
33
2
38
4
44
15
49
16
55
19
60
29
66
25
72
10
77
7
10th25th Median 75th90th 95th99th
45 53 61 67 72 77 78
OS+ n=133 · μ=126 · σ=40.739
77
29
90
34
103
0
116
0
129
30
141
13
154
0
167
17
180
0
193
8
206
0
219
2
10th25th Median 75th90th 95th99th
77 103 129 154 180 206 223
GE n=133 · μ=0.2 · σ=0.059
0.1
29
0.1
0
0.1
34
0.2
0
0.2
30
0.2
0
0.2
13
0.2
0
0.3
17
0.3
0
0.3
8
0.3
2
10th25th Median 75th90th 95th99th
0.1 0.1 0.2 0.2 0.3 0.3 0.3
K% n=133 · μ=30.2% · σ=18.4%
0.0%
13
8.3%
26
16.7%
6
25.0%
31
33.3%
16
41.7%
18
50.0%
15
58.3%
5
66.7%
1
75.0%
1
83.3%
0
91.7%
1
10th25th Median 75th90th 95th99th
11.4% 14.3% 28.6% 42.9% 55.6% 60.0% 75.7%
BB% n=133 · μ=14.2% · σ=13.8%
0.0%
49
4.8%
0
9.5%
16
14.3%
24
19.0%
5
23.8%
10
28.6%
18
33.3%
5
38.1%
1
42.9%
2
47.6%
0
52.4%
3
10th25th Median 75th90th 95th99th
0.0% 0.0% 14.3% 25.0% 28.6% 37.5% 56.6%
HR% n=133 · μ=1.7% · σ=5.0%
0.0%
118
2.1%
0
4.2%
0
6.2%
0
8.3%
0
10.4%
3
12.5%
9
14.6%
0
16.7%
0
18.8%
1
20.8%
0
22.9%
2
10th25th Median 75th90th 95th99th
0.0% 0.0% 0.0% 0.0% 11.1% 14.3% 23.4%
K-BB% n=133 · μ=7.7% · σ=24.2%
-57.1%
1
-46.1%
4
-35.1%
10
-24.1%
15
-13.1%
6
-2.1%
29
8.9%
28
19.9%
20
31.0%
6
42.0%
11
53.0%
1
64.0%
2
10th25th Median 75th90th 95th99th
-25.0% -12.5% 11.1% 22.2% 42.3% 50.0% 64.5%
oAVG n=133 · μ=0.407 · σ=0.214
0.000
11
0.083
3
0.167
13
0.250
17
0.333
24
0.417
8
0.500
31
0.583
9
0.667
11
0.750
2
0.833
2
0.917
2
10th25th Median 75th90th 95th99th
0.148 0.250 0.400 0.571 0.667 0.714 0.954
WAR n=133 · μ=0.07 · σ=0.086
-0.12
4
-0.09
4
-0.05
16
-0.02
15
0.01
20
0.04
12
0.07
17
0.11
16
0.14
10
0.17
8
0.20
8
0.23
3
10th25th Median 75th90th 95th99th
-0.04 -0.00 0.06 0.12 0.18 0.21 0.26
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.
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