Datasets:

yasumorishima
/

mlb-stats

entity_name large_stringlengths 9 22 ⌀	player_id int64 150k 806k	team_id float64 108 158 ⌀	age float64 20 39 ⌀	maxeff_arm_2b_3b_sba float64 72.3 92.8 ⌀	exchange_2b_3b_sba float64 0.53 0.87 ⌀	pop_2b_sba_count float64 5 73 ⌀	pop_2b_sba float64 1.81 2.17 ⌀	pop_2b_cs float64 1.79 2.42 ⌀	pop_2b_sb float64 1.78 2.19 ⌀	pop_3b_sba_count float64 0 10 ⌀	pop_3b_sba float64 1.35 2.38 ⌀	pop_3b_cs float64 1.34 2.29 ⌀	pop_3b_sb float64 1.33 2.38 ⌀	season int64 2.02k 2.03k	fr_last_name large_stringclasses 83 values	fr_first_name large_stringclasses 75 values	fr_year float64 2.02k 2.02k ⌀	fr_n_called_pitches float64 993 4.35k ⌀	fr_runs_extra_strikes float64 -17 31 ⌀	fr_strike_rate float64 35.8 54.3 ⌀	fr_strike_rate_11 float64 2 36.8 ⌀	fr_strike_rate_12 float64 11.6 54.4 ⌀	fr_strike_rate_13 float64 1.8 27.9 ⌀	fr_strike_rate_14 float64 56 87.1 ⌀	fr_strike_rate_16 float64 43.2 82.2 ⌀	fr_strike_rate_17 float64 18.9 54.1 ⌀	fr_strike_rate_18 float64 23.3 69.9 ⌀	fr_strike_rate_19 float64 6.1 52.4 ⌀
Pierzynski, A.J.	150,229	144	38	78.7	0.75	46	2.07	2.07	2.06	3	1.62	1.64	1.6	2,015	Pierzynski	A.J.	2,015	2,990	-8	44.6	21.8	39.5	14.4	77.3	57.6	37.6	32.7	15.7
Pierzynski, A.J.	150,229	144	39	77.9	0.77	29	2.09	2.1	2.09	4	1.64	1.65	1.6	2,016	Pierzynski	A.J.	2,016	1,840	-8	41.7	13.2	30.6	9.8	73.1	55.8	31.5	41.1	14.2
Ross, David	424,325	112	38	80.6	0.68	31	1.96	1.97	1.96	4	1.55	1.6	1.54	2,015	Ross	David	2,015	1,326	3	52.3	22.4	30.2	6	76	68.2	39.3	49.1	32.9
Ross, David	424,325	112	39	80	0.66	27	1.95	1.96	1.94	3	1.52	1.46	1.54	2,016	Ross	David	2,016	1,534	3	50	15.3	37.7	5.3	68.8	71.8	36.8	44.1	34.1
Mathis, Jeff	425,772	146	32	78.5	0.74	6	2.07	2.02	2.08	2	1.61	1.63	1.59	2,015	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Mathis, Jeff	425,772	146	33	79.2	0.75	17	2.08	2.07	2.08	2	1.61	null	1.61	2,016	Mathis	Jeff	2,016	1,050	5	53.1	14	29.8	8.3	70.1	66.5	44.2	58.1	45.9
Mathis, Jeff	425,772	109	34	78.5	0.72	16	2.05	2.07	2.03	0	null	null	null	2,017	Mathis	Jeff	2,017	1,552	5	54.3	8.6	11.6	3.8	60.4	70.1	50.9	69.9	52.4
Mathis, Jeff	425,772	109	35	78.6	0.74	16	2.07	2.12	2.05	1	1.6	1.6	null	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Mathis, Jeff	425,772	140	36	76.5	0.77	24	2.12	2.12	2.12	3	1.78	null	1.78	2,019	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Mathis, Jeff	425,772	140	37	79.5	0.7	11	1.98	1.99	1.97	0	null	null	null	2,020	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Rivera, René	425,784	139	31	82.2	0.7	45	1.97	1.98	1.96	0	null	null	null	2,015	Rivera	René	2,015	2,507	7	49.3	23.7	35.3	5.7	80.2	59.3	48	50.4	21.7
Rivera, René	425,784	121	32	81.5	0.69	27	1.99	2.03	1.96	5	1.55	1.5	1.58	2,016	Rivera	René	2,016	1,572	6	50.1	22.4	40.8	14.5	72.8	70.8	40.5	42.8	25.9
Rivera, René	425,784	112	33	81.4	0.67	23	1.99	1.99	1.99	3	1.53	1.56	1.52	2,017	Rivera	René	2,017	2,014	3	47.8	12.5	34.1	14.5	62.5	69.9	29.9	51.9	37.9
Rivera, René	425,784	144	34	81.6	0.72	10	2	1.97	2.02	0	null	null	null	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Rivera, René	425,784	120	37	80.4	0.69	15	1.97	2	1.93	2	1.43	null	1.43	2,021	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Molina, Yadier	425,877	138	32	83.7	0.74	27	1.97	1.98	1.96	2	1.6	null	1.6	2,015	Molina	Yadier	2,015	3,581	9	48.6	19.1	44.6	13.1	76.7	62.2	36.5	43.1	20.3
Molina, Yadier	425,877	138	33	83.6	0.7	38	1.96	1.94	1.97	7	1.64	1.58	1.66	2,016	Molina	Yadier	2,016	4,093	10	47	19.5	46.5	11.8	71.6	61.7	33.4	44.1	22
Molina, Yadier	425,877	138	34	83.3	0.73	28	1.97	2	1.93	5	1.57	1.59	1.53	2,017	Molina	Yadier	2,017	3,781	7	49.2	22.1	40.4	22.3	66.3	67.5	39.3	50.3	26.6
Molina, Yadier	425,877	138	35	82.2	0.76	14	2.05	2.05	2.04	0	null	null	null	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Molina, Yadier	425,877	138	36	84.1	0.69	12	1.98	1.95	2	1	1.45	null	1.45	2,019	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Molina, Yadier	425,877	138	37	81.7	0.71	9	1.95	1.97	1.93	0	null	null	null	2,020	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Molina, Yadier	425,877	138	38	83.2	0.69	24	1.91	1.9	1.91	1	2.38	null	2.38	2,021	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Molina, Yadier	425,877	138	39	79.9	0.77	19	2.05	2.03	2.06	1	1.95	1.95	null	2,022	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Navarro, Dioner	425,900	141	31	80.8	0.71	8	2.01	1.98	2.12	1	1.51	null	1.51	2,015	Navarro	Dioner	2,015	1,068	-2	43.5	24.2	41.2	20	77.9	58.5	30.6	28.6	14.6
Navarro, Dioner	425,900	141	32	81.3	0.73	27	2.03	2	2.05	0	null	null	null	2,016	Navarro	Dioner	2,016	2,669	-10	43.4	22.3	43.2	26.8	67	66.5	18.9	28.3	12.9
Peña, Brayan	430,910	113	33	80.1	0.81	37	2.14	2.12	2.15	2	1.69	null	1.69	2,015	Peña	Brayan	2,015	2,489	-5	43.7	21	34.2	15.8	72	58.9	36.2	34.9	19.5
Martin, Russell	431,145	141	32	86.2	0.74	34	1.97	1.98	1.94	2	1.47	1.48	1.47	2,015	Martin	Russell	2,015	3,432	-2	46	26.8	36.1	10.9	77.5	55.3	37.1	44.9	16.4
Martin, Russell	431,145	141	33	84.2	0.77	31	2.02	1.99	2.03	5	1.55	1.53	1.58	2,016	Martin	Russell	2,016	3,722	14	48	18.2	37.8	14.9	79.9	59.5	43.1	46.8	18.4
Martin, Russell	431,145	141	34	81.6	0.71	27	2.01	2.07	2	7	1.66	1.56	1.78	2,017	Martin	Russell	2,017	2,272	3	49.5	23.8	38	26.6	63.4	70.7	28	49.7	32.5
Martin, Russell	431,145	141	35	83.1	0.74	39	2.01	2.08	1.99	4	1.59	1.61	1.57	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Martin, Russell	431,145	119	36	83.6	0.75	9	2.04	1.92	2.07	0	null	null	null	2,019	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Ruiz, Carlos	434,563	143	36	79.5	0.78	31	2.1	2.09	2.1	1	1.65	null	1.65	2,015	Ruiz	Carlos	2,015	2,424	-11	42.9	14.3	32.2	12.6	72	49.4	36.9	46.1	11.6
Ruiz, Carlos	434,563	119	37	80.3	0.79	19	2.09	2.09	2.09	3	1.63	1.63	1.63	2,016	Ruiz	Carlos	2,016	1,743	-8	42.6	16.9	28.4	8.2	78.5	43.2	36.7	42.4	13.1
Ruiz, Carlos	434,563	136	38	79	0.78	14	2.08	2.13	2.06	3	1.61	1.57	1.63	2,017	Ruiz	Carlos	2,017	1,195	-5	41.3	15.3	16.4	14.3	62	53	26.5	51.2	20.7
Soto, Geovany	434,567	145	32	80.5	0.77	20	2.04	2.04	2.03	0	null	null	null	2,015	Soto	Geovany	2,015	1,524	-1	46.9	11.7	33.8	11.7	74.4	62.8	29.6	48.9	29.8
Soto, Geovany	434,567	108	33	76.3	0.7	18	2.08	2.09	2.08	3	1.61	null	1.61	2,016	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Soto, Geovany	434,567	145	34	78.5	0.7	6	2.04	2.02	2.05	0	null	null	null	2,017	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Wilson, Bobby	435,064	140	32	79.6	0.74	22	2.03	2.03	2.03	0	null	null	null	2,015	Wilson	Bobby	2,015	1,293	3	47.2	23.9	38.3	14.8	79.7	56.9	30.9	50.3	22.7
Wilson, Bobby	435,064	139	33	77.4	0.71	28	2.05	2.04	2.05	1	1.53	null	1.53	2,016	Wilson	Bobby	2,016	2,081	-2	44.4	19	34.3	8.8	78.7	54.5	26.9	46.4	18.5
Wilson, Bobby	435,064	142	35	77.8	0.71	16	2.09	2.08	2.09	1	1.78	1.78	null	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Wilson, Bobby	435,064	116	36	76.3	0.76	8	2.08	2	2.1	1	1.62	null	1.62	2,019	null	null	null	null	null	null	null	null	null	null	null	null	null	null
McCann, Brian	435,263	147	31	79	0.75	39	2.05	2.07	2.03	4	1.68	1.66	1.74	2,015	McCann	Brian	2,015	3,562	-5	45.9	21.3	22.7	4.5	82.5	48.7	44.5	46.4	15.7
McCann, Brian	435,263	147	32	79.3	0.73	34	2.04	2.04	2.03	1	1.53	1.53	null	2,016	McCann	Brian	2,016	2,514	0	47.8	11.7	24.9	5.7	75.9	54	49	51.9	23.1
McCann, Brian	435,263	117	33	77.8	0.76	23	2.1	2.14	2.09	2	1.93	1.6	2.27	2,017	McCann	Brian	2,017	3,264	-1	49.1	10.9	18.1	13.2	70.3	62.4	48.9	59.3	31.5
McCann, Brian	435,263	117	34	77.2	0.77	16	2.08	2.08	2.08	1	1.65	null	1.65	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
McCann, Brian	435,263	144	35	77.2	0.78	20	2.08	2.08	2.08	3	1.63	1.66	1.62	2,019	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Suzuki, Kurt	435,559	142	31	81.8	0.84	42	2.09	2.09	2.09	2	1.57	null	1.57	2,015	Suzuki	Kurt	2,015	3,566	-10	45.8	13	25.7	8.1	75	59.5	41.3	44.1	23.4
Suzuki, Kurt	435,559	142	32	81.4	0.84	27	2.11	2.1	2.11	5	1.63	1.61	1.65	2,016	Suzuki	Kurt	2,016	2,829	1	46.9	19.8	45.1	17.5	73.6	61.7	31.9	36.8	17.8
Suzuki, Kurt	435,559	144	33	81.4	0.81	20	2.07	2.07	2.07	3	1.7	1.78	1.54	2,017	Suzuki	Kurt	2,017	2,115	-5	46.1	14.5	36.4	17	56	72.3	25.2	44.1	28
Suzuki, Kurt	435,559	144	34	80.3	0.81	24	2.08	2.1	2.07	4	1.65	1.68	1.63	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Suzuki, Kurt	435,559	120	35	81.1	0.79	20	2.06	2.04	2.07	3	1.65	1.69	1.63	2,019	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Suzuki, Kurt	435,559	120	36	78	0.8	16	2.04	2.05	2.04	0	null	null	null	2,020	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Suzuki, Kurt	435,559	108	37	81.1	0.77	26	1.99	2.01	1.98	4	1.55	null	1.55	2,021	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Suzuki, Kurt	435,559	108	38	80.2	0.79	16	2.07	2.07	2.07	1	1.63	null	1.63	2,022	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Piña, Manny	444,489	158	29	84.7	0.72	9	1.97	2.01	1.94	1	1.59	null	1.59	2,016	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Piña, Manny	444,489	158	30	85	0.68	41	1.94	1.96	1.92	1	1.46	1.46	null	2,017	Piña	Manny	2,017	2,532	-5	46.8	20.1	37.3	15.6	69.2	71	31.4	40.3	22.5
Piña, Manny	444,489	158	31	83.9	0.67	26	1.95	1.93	1.98	0	null	null	null	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Piña, Manny	444,489	158	32	83.2	0.66	16	1.95	1.94	1.96	2	1.63	1.58	1.69	2,019	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Piña, Manny	444,489	158	33	82.5	0.74	8	2	2	1.99	0	null	null	null	2,020	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Piña, Manny	444,489	158	34	81	0.72	21	1.94	1.91	1.97	1	1.57	null	1.57	2,021	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Moore, Adam	446,192	139	34	84.2	0.81	7	2.02	2.02	2.02	1	1.67	null	1.67	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Wieters, Matt	446,308	110	29	81	0.71	17	1.96	1.99	1.95	1	1.5	1.5	null	2,015	Wieters	Matt	2,015	1,646	0	46.2	28.3	41.7	15.9	82.5	44.9	42	40.1	8.3
Wieters, Matt	446,308	110	30	81.6	0.71	41	1.99	1.99	1.99	1	1.7	null	1.7	2,016	Wieters	Matt	2,016	3,508	-10	43	26.9	35.9	10.9	83.4	48.5	36.6	34.3	11.5
Wieters, Matt	446,308	120	31	81	0.75	32	2.02	2.04	2	3	1.58	1.56	1.63	2,017	Wieters	Matt	2,017	3,456	-12	43.5	21.4	34.6	15	66.2	58.8	28.1	42.9	18.3
Wieters, Matt	446,308	120	32	79.8	0.72	22	2.01	1.99	2.02	4	1.56	1.55	1.58	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Wieters, Matt	446,308	138	33	79.3	0.71	10	2.02	2.02	2	0	null	null	null	2,019	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Lobatón, Jose	446,653	120	30	80.9	0.77	14	2.06	2.06	2.06	1	1.64	null	1.64	2,015	Lobatón	Jose	2,015	1,099	3	52	20	28	13.7	80	61.3	49.4	51.8	35.9
Lobatón, Jose	446,653	120	31	82.7	0.73	6	2	2.01	2	2	1.64	1.64	null	2,016	Lobatón	Jose	2,016	1,011	4	51.5	20.9	31.8	1.8	77.5	59	54.1	59	31.4
Lobatón, Jose	446,653	120	32	81.7	0.76	16	2.01	2.03	2.01	0	null	null	null	2,017	Lobatón	Jose	2,017	1,331	1	47	18.3	33.6	7.9	66.7	62.4	34.1	48.6	33
Lobatón, Jose	446,653	121	33	81	0.77	5	2.06	2.01	2.07	1	1.67	1.67	null	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Corporán, Carlos	449,786	140	31	80.9	0.78	11	2.03	1.98	2.05	0	null	null	null	2,015	Corporán	Carlos	2,015	1,004	2	48.2	25	41	8.2	78.7	54.5	48.6	46.5	19.1
Arencibia, J.P.	450,317	139	29	82.1	0.72	6	2.02	1.99	2.04	0	null	null	null	2,015	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Hayes, Brett	451,109	114	31	82.4	0.77	5	2	2.04	2	0	null	null	null	2,015	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Rosario, Alberto	451,705	138	29	81.9	0.75	6	1.96	2.01	1.93	2	1.63	1.57	1.68	2,016	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Flowers, Tyler	452,095	145	29	77.5	0.78	24	2.12	2.14	2.11	2	1.72	1.73	1.71	2,015	Flowers	Tyler	2,015	2,876	10	50.4	9.6	35.3	11.9	69.8	67.6	41.6	54.8	32.1
Flowers, Tyler	452,095	144	30	76.7	0.74	34	2.13	2.16	2.12	3	1.66	null	1.66	2,016	Flowers	Tyler	2,016	2,249	9	50.5	9.8	29.8	7.9	76.3	58.7	49.4	62.7	29.3
Flowers, Tyler	452,095	144	31	74.7	0.75	28	2.14	2.19	2.12	4	1.66	1.66	null	2,017	Flowers	Tyler	2,017	2,400	16	54.3	14.8	31.4	15.1	65	82.2	38	62.2	46.2
Flowers, Tyler	452,095	144	32	74.8	0.75	25	2.14	2.08	2.15	3	1.72	1.7	1.73	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Flowers, Tyler	452,095	144	33	75.9	0.74	23	2.12	2.09	2.13	1	1.58	null	1.58	2,019	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Flowers, Tyler	452,095	144	34	73.6	0.77	13	2.11	2.11	2.11	0	null	null	null	2,020	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Hanigan, Ryan	452,672	111	34	81.1	0.78	15	2.06	2.09	2.05	3	1.65	1.65	null	2,015	Hanigan	Ryan	2,015	1,583	1	48.7	20.2	38.8	15.7	75.7	60.7	41.6	51.5	23.4
Hanigan, Ryan	452,672	111	35	79.7	0.73	14	2.07	2.08	2.07	3	1.64	1.62	1.65	2,016	Hanigan	Ryan	2,016	995	-5	40.2	19.1	46	22.2	72.5	46.6	20.7	34.4	7.2
Hanigan, Ryan	452,672	115	36	78.9	0.75	8	2.02	2.02	2.02	2	1.6	null	1.6	2,017	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Ellis, A.J.	454,560	119	34	80	0.72	15	2.04	2.09	2.01	3	1.59	1.57	1.64	2,015	Ellis	A.J.	2,015	1,616	-8	41	24.1	51.2	10.4	72.1	59.9	19.9	23.3	10.3
Ellis, A.J.	454,560	143	35	77.3	0.73	22	2.09	2.11	2.09	0	null	null	null	2,016	Ellis	A.J.	2,016	1,531	1	44.8	21.2	40	10.6	78.6	58.6	31.9	36	16
Ellis, A.J.	454,560	146	36	76.9	0.76	11	2.09	2.08	2.1	1	1.67	null	1.67	2,017	Ellis	A.J.	2,017	1,073	-2	43.3	16.2	44.5	15.8	67.9	62.4	26.5	39.6	12.8
Ellis, A.J.	454,560	135	37	78	0.69	18	2.06	2.04	2.06	2	1.57	1.57	null	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Iannetta, Chris	455,104	108	32	82.9	0.8	29	2.05	2.05	2.05	5	1.62	1.61	1.62	2,015	Iannetta	Chris	2,015	2,456	9	48.9	28.3	44.1	14.8	75.1	63.3	37.6	45.7	23.3
Iannetta, Chris	455,104	136	33	81.8	0.78	42	2.05	2.05	2.06	2	1.66	1.68	1.63	2,016	Iannetta	Chris	2,016	2,722	-14	42	13.6	34.6	8.3	69.4	56.4	24.1	39.2	20.2
Iannetta, Chris	455,104	109	34	82	0.79	14	2.05	2.05	2.05	1	1.58	1.58	null	2,017	Iannetta	Chris	2,017	2,185	1	48.1	6.7	27.3	11.2	62.4	69.8	33.9	53.3	39.5
Iannetta, Chris	455,104	115	35	81.4	0.79	20	2.04	2.06	2.04	2	1.6	null	1.6	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Iannetta, Chris	455,104	115	36	80.2	0.75	8	2.08	2.04	2.1	1	1.63	null	1.63	2,019	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Maldonado, Martín	455,117	158	28	88.1	0.75	25	1.92	1.92	1.92	2	1.56	null	1.56	2,015	Maldonado	Martín	2,015	2,066	3	48.5	15.8	30.6	16.7	77.7	62.8	37.6	48.6	29.7
Maldonado, Martín	455,117	158	29	87.3	0.7	20	1.91	1.93	1.88	3	1.54	1.6	1.51	2,016	Maldonado	Martín	2,016	1,939	4	48.8	19.1	34.2	7.3	78.8	57.1	49.4	51.7	23.4
Maldonado, Martín	455,117	108	30	87.9	0.75	38	1.93	1.94	1.93	3	1.61	1.7	1.56	2,017	Maldonado	Martín	2,017	3,716	21	52	16	38.1	16.2	64.3	74.4	38.5	57.1	37.4
Maldonado, Martín	455,117	117	31	87.6	0.77	20	1.97	1.98	1.97	4	1.61	1.58	1.62	2,018	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Maldonado, Martín	455,117	117	32	87.3	0.76	27	1.96	1.96	1.97	0	null	null	null	2,019	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Maldonado, Martín	455,117	117	33	84.5	0.74	12	1.91	1.92	1.9	0	null	null	null	2,020	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Maldonado, Martín	455,117	117	34	84.6	0.78	32	1.96	1.99	1.93	2	1.48	1.51	1.46	2,021	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Maldonado, Martín	455,117	117	35	86	0.74	40	1.92	1.93	1.92	2	1.48	1.48	null	2,022	null	null	null	null	null	null	null	null	null	null	null	null	null	null

End of preview. Expand in Data Studio

MLB Shared Stats

Season-level MLB tables, refreshed weekly by yasumorishima/mlb-data-pipeline and published here as Parquet. One file per table at the repository root.

Read the freshness column before using a table. Not everything here is current, and the rows in a stale table look exactly like the rows in a fresh one.

Tables

File	Source	Seasons	Refreshed
`sc_batter_exitvelo.parquet`	Baseball Savant	2015–	weekly
`sc_pitcher_exitvelo.parquet`	Baseball Savant	2015–	weekly
`sc_batter_expected.parquet`	Baseball Savant	2015–	weekly
`sc_pitcher_expected.parquet`	Baseball Savant	2015–	weekly
`sc_pitcher_arsenal.parquet`	Baseball Savant	2017–	weekly
`sc_batted_ball.parquet`	Baseball Savant	2015–	weekly
`sc_bat_tracking.parquet`	Baseball Savant	2024– (Hawk-Eye)	weekly
`sprint_speed.parquet`	Baseball Savant	2015–	weekly
`oaa.parquet`	Baseball Savant	2016–	weekly
`oaa_team.parquet`	Baseball Savant	2016–	weekly
`catcher.parquet`	Baseball Savant	2015–	weekly
`park_factors.parquet`	Baseball Savant	2015–	weekly (added 2026-09-23)
`statsapi_batting.parquet`	MLB Stats API	2015–	weekly (added 2026-09-23)
`statsapi_pitching.parquet`	MLB Stats API	2015–	weekly (added 2026-09-23)
`fg_batting.parquet`	FanGraphs	2015–2025	⚠️ frozen 2026-04
`fg_pitching.parquet`	FanGraphs	2015–2025	⚠️ frozen 2026-04
`fg_pitcher_plus.parquet`	FanGraphs	2020–2025	⚠️ frozen 2026-04

statcast_pitches (pitch-level, ~6.8M rows) is fetched by manual dispatch only and is not part of the weekly refresh.

Marts (`marts/`)

Analysis-ready tables built from the tables above by the dbt project and republished after every weekly refresh, only when all of its data tests pass. A build that fails leaves the previous marts in place, so they can lag the raw tables: marts/_manifest.json records when they were built, the pipeline commit, and the revision of this dataset they were built from. Rows for a season whose regular season is not over carry is_partial = true.

File	Grain
`marts/mart_batter_season.parquet`	batter-season (PA > 0): wOBA, wRC+, WAR next to xwOBA, batted-ball mix, bat speed, sprint speed, OAA
`marts/mart_pitcher_season.parquet`	pitcher-season: K%, BB%, K-BB%, FIP, xERA, pitch-mix breadth
`marts/mart_batter_aging_pairs.parquet`	same batter, season and season + 1: input for aging curves
`marts/mart_pitch_arsenal_scouting.parquet`	pitcher-season-pitch: usage rank, whiff and run-value percentiles within type
`marts/mart_scouting_reliability.parquet`	metric x sample-size bin: year-to-year correlation of each pitch metric, raw and within pitch type
`marts/mart_batter_process_reliability.parquet`	metric x PA bin: year-to-year correlation of each batting number and its correlation with next season's wOBA
`marts/mart_pitcher_process_reliability.parquet`	metric x BF bin: year-to-year correlation of each pitching number and its correlation with next season's ERA
`marts/mart_fielding_running_reliability.parquet`	metric x position or competitive-run bin: year-to-year correlation of OAA, fielding runs prevented, catch rate above expected, sprint speed and home to first

Why the three FanGraphs tables are frozen

FanGraphs refuses the GitHub Actions runner. The cause is the address, not the client: pybaseball sets no User-Agent, so it sends the honest python-requests default, and it still got HTTP 403 for all 12 seasons on run 35565978836 (2026-09-21). The same request from a home connection returns 200.

There is a second, separate layer that is easy to confuse with it: a client claiming to be a browser gets 403 with cf-mitigated: challenge from anywhere, including a residential line. Measured 2026-09-23.

So these three tables hold a rescue snapshot taken in 2026-04, which ends with the 2025 season. They contain no 2026 rows. The pipeline reports them on every run and the exemption has an expiry date, so this cannot quietly become permanent.

For current wOBA / wRC+ / WAR / FIP / xFIP, use statsapi_batting and statsapi_pitching below. They are not FanGraphs tables.

`statsapi_batting` / `statsapi_pitching`

From the MLB Stats API, keyless: stats=season (counting and rate stats) joined with stats=sabermetrics (wOBA, wRAA, wRC, wRC+, WAR and its components for hitters; FIP, xFIP, FIP-, ERA-, WAR, RA9-WAR, leverage for pitchers), playerPool=ALL. Column names are the API's own (plateAppearances, wRcPlus, xfip, …). Responses carry "Copyright MLB Advanced Media, L.P." and point to the terms at http://gdx.mlb.com/components/copyright.txt.

MLB does not document how its sabermetrics feed is computed or where it comes from. What we measured, 2026-09-23, joining on MLBAM player_id against the frozen FanGraphs snapshot above (every one of its 5,703 hitter and 4,648 pitcher rows matched):

Counting stats agree exactly (HR, SO; PA within 1).
2015–2021: wOBA within 0.002, wRC+ within 0.5 (FanGraphs stores integers), WAR within 0.2, FIP / xFIP within 0.005 — i.e. rounding.
2022–2025: wOBA still within 0.006, but wRC+ differs by up to 1.0 (2022, 2023), 3.4 (2024) and 5.6 (2025), and the 2025 difference is lined up by club (ATH +5.1, CIN −3.1). Park factors were revised after the snapshot. WAR follows: up to 0.34 for hitters in 2024, and 0.39 for hitters and 0.40 for pitchers in 2025. One 2024 pitcher differs by 0.31 in FIP and 0.32 in xFIP; the other 433 by ≤ 0.005.

So past seasons can change from one week to the next; every run refetches every season.

Rows and columns:

One row per (player_id, season), every player who appeared, not a qualified subset (e.g. 1,252 hitters in 2015 against 546 in the FanGraphs snapshot). The hitter count falls from ~1,250 to ~770 in 2022 because the universal DH ended pitchers batting.
A player who changed clubs has one row with his season total. last_team_id is the club he finished with and num_teams how many he played for. It is not "his stats for that club".
is_partial is true for the current season until every regular-season game is final. fetched_at is when the row was read.
inningsPitched is kept as the API's string: "5.1" means five and one third innings, not 5.1. Use outs / 3.
Rates are absent where the denominator is zero: woba / wRcPlus for hitters with 0 PA (288 of 1,252 in 2015), fip / xfip for a pitcher with 0 outs. Placeholder strings such as ".---" become null.
In the current season a pitcher can appear in stats=season before stats=sabermetrics (one in 2026); his sabermetric columns are null.

The fetch fails and these two tables are not published if a response was truncated (totalSplits ≠ rows returned), answered for another season, lacks a required stat, repeats a player, has a player only the sabermetrics side knows, or if a completed season now has fewer players than the copy already published here. A run that checked the ids and knows MLB removed a player (e.g. merged a duplicate id) can publish with the allow_statsapi_shrink workflow input.

For every table except statcast_pitches (one file per season, so a narrower run cannot remove the others), the output audit also refuses to publish a file that lacks a season the published copy has, so a manual run over a narrower year range cannot delete seasons from here. A deliberate removal has to name the table in the allow_lost_seasons workflow input.

The weekly run covers seasons up to the latest one in which every club has played a game, read from the Stats API standings; before that (and through an overseas opening series) it stops at the previous season.

Why you can trust the "weekly" column

Every run audits what it produced. A table that fetched nothing used to be skipped silently — the upload step pushes whatever files exist, so the copy here simply stayed as it was and the job still went green. That is how park_factors never arrived at all until 2026-09-23, and how the FanGraphs tables went stale without an alarm.

Now scripts/check_outputs.py prints one row per expected table, publishes only the tables that passed, and turns the run red otherwise.

`park_factors` columns

One row per club per season, all 30 clubs in every season. 100 = neutral, above 100 favours hitters. From Baseball Savant's Statcast park factors.

season, team, venue_id, venue_name, n_pa_1yr, n_pa_3yr, pf_1yr, pf_3yr, pf_3yr_years, then pf_hr, pf_1b, pf_2b, pf_3b, pf_so, pf_bb, pf_obp, pf_hits, pf_woba, pf_wobacon, pf_xwobacon, pf_bacon, pf_xbacon, pf_hardhit, pf_wobatto.

Everything except pf_1yr and n_pa_1yr comes from a 3-year window; pf_3yr_years records which window that was.

Two NaN patterns are expected, not defects:

8 rows of 360 have no 3-year window at all, because the park has no three-year history: 2017 ATL, 2018 ATL, 2020 TEX, 2020 TOR, 2021 TEX, 2025 OAK, 2025 TB, 2026 OAK. Their pf_3yr* columns are NaN while pf_1yr is filled. The club is kept rather than dropped, so a team never silently disappears from a season.
pf_xwobacon, pf_xbacon and pf_hardhit are NaN for all 30 clubs in 2015 and 2016, because those windows reach back before Statcast measured batted balls. Those three columns have 68 NaN, not 8.

Subdirectories

mlb_bat_tracking/ and mlb_wp/ come from other projects and are not touched by the weekly refresh. Their freshness is unrelated to the table above.

Usage

import pandas as pd

base = "https://huggingface.co/datasets/yasumorishima/mlb-stats/resolve/main/"
pf = pd.read_parquet(base + "park_factors.parquet")
print(pf[pf["season"] == 2026].nlargest(3, "pf_3yr")[["team", "venue_name", "pf_3yr"]])

Sources and terms

Baseball Savant — MLB Advanced Media
FanGraphs — the three fg_* tables
MLB Stats API — statsapi_batting and statsapi_pitching; responses state "Copyright MLB Advanced Media, L.P." and refer to http://gdx.mlb.com/components/copyright.txt
Fetched with pybaseball and savant-extras

This is a derived, aggregated mirror published for research and reproducibility. No licence is asserted over the underlying data, which remains subject to the terms of the sources above; check those before redistributing. Open an issue on the pipeline repository for corrections.

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