Quantifying the Impact of K and BB Rates on Offensive Production

When evaluating a hitter or pitcher’s statistical performance, the vast majority of what you need to know comes from the following rate data – strikeouts, walks, popups, line drives, hard and weak flyballs, and hard and weak groundballs. Put some of those on the good side of the ledger, the rest on the bad, and pretty quickly one has a feel for the present ability, and along with scouting data, the future projection of the player. Today, let’s focus on strikeouts and walks, and on quantifying their overall impact on hitters’ offensive production. How much must a hitter do to compensate for poor K and BB rates, and much additional margin for error is provided by strong K and BB rates.

Our control group will be 34 position players who have changed clubs this offseason and logged substantial playing time in 2013. First, let’s take a look at these players’ 2013 strikeout/walk data expressed in a few different ways.

Last	First		K	BB	K %	BB %	K %:100	BB%:100	K PCTILE	BB PCTILE	GROUP
Aoki	Norichika		40	55	6.1%	8.3%	30	103	1	55	1
Arencibia	JP		148	18	30.0%	3.7%	150	46	95	4	4
Barnes	Brandon		127	21	29.1%	4.8%	143	60	89	14	4
Beltran	Carlos		90	38	15.3%	6.5%	75	80	27	27	3
Byrd	Marlon		144	31	25.5%	5.5%	125	68	87	15	4
Cano	Robinson		85	65	12.7%	9.7%	63	121	18	70	1
Choo	Shin-Soo		133	112	19.4%	16.4%	94	199	57	98	1
Davis	Rajai		67	21	18.9%	5.9%	95	75	53	20	3
Doumit	Ryan		99	48	18.6%	9.0%	92	113	52	64	1
Ellis	Mark		74	26	15.8%	5.5%	77	69	30	16	3
Ellsbury	Jacoby		92	47	14.8%	7.6%	73	94	29	37	3
Fielder	Prince		117	75	16.7%	10.7%	83	133	41	81	1
Fowler	Dexter		105	65	21.7%	13.4%	107	167	71	92	2
Freese	David		106	47	20.9%	9.3%	102	114	66	66	2
Ibanez	Raul		128	42	26.1%	8.6%	129	107	81	56	2
Infante	Omar		44	20	9.3%	4.2%	46	53	2	11	3
Johnson	Kelly		99	35	24.6%	8.7%	122	109	76	58	2
Jones	Garrett		101	31	23.4%	7.2%	115	89	78	36	4
Kinsler	Ian		59	51	9.8%	8.5%	48	105	5	55	1
Lough	David		52	10	15.8%	3.0%	78	38	35	2	3
McCann	Brian		66	39	16.7%	9.8%	83	123	38	73	1
McLouth	Nate		86	53	14.7%	9.1%	73	113	30	65	1
Morneau	Justin		110	50	17.6%	8.0%	87	100	44	49	1
Morrison	Logan		56	38	17.1%	11.6%	86	146	45	89	1
Murphy	David		59	37	12.5%	7.8%	62	98	16	46	3
Peralta	Jhonny		98	35	22.0%	7.8%	110	99	68	47	4
Pierzynski	AJ		76	11	14.8%	2.1%	72	26	27	1	3
Ruggiano	Justin		114	41	24.8%	8.9%	121	110	85	64	2
Saltalamacchia	Jarrod		139	43	29.8%	9.2%	149	116	94	67	2
Schumaker	Skip		54	28	15.4%	8.0%	78	102	31	53	1
Smith	Seth		94	39	23.4%	9.7%	115	120	71	68	2
Stubbs	Drew		141	44	29.7%	9.3%	147	116	90	66	2
Trumbo	Mark		184	54	27.1%	8.0%	136	101	85	49	2
Young	Chris		93	36	25.1%	9.7%	126	123	79	70	2

The table above lists each player’s raw K and BB totals, their K and BB rates, those rates scaled to MLB average of 100, and expressed in overall percentile rank within the MLB regular player population. The players are also placed in groups (far right column) as follows: Group 1 = K rate below MLB average, BB rate above MLB average; Group 2 = K and BB rates below MLB average; Group 3 = K and BB rates above MLB average, and Group 4 = K rate above MLB average, BB rate below MLB average. This table alone, however, tells us a limited amount about each player’s offensive ability. Let’s fill in the picture a little more by adding performance on balls put in play (including homers) for each player, and then quantify the impact of the K and BB data on each hitter, and each group of hitter.

Last	First		BIP AVG	BIP SLG	BIP RUN	BIP R:100	TOT AVG	TOT OBP	TOT SLG	TOT RUN	TOT R:100	GROUP
Aoki	Norichika		0.299	0.387	3.54	72	0.279	0.339	0.361	3.88	100	1
Arencibia	JP		0.284	0.532	4.54	93	0.196	0.225	0.366	2.48	64	4
Barnes	Brandon		0.336	0.484	4.91	100	0.233	0.270	0.337	2.79	72	4
Beltran	Carlos		0.350	0.580	6.09	124	0.293	0.338	0.485	5.03	130	2
Byrd	Marlon		0.397	0.695	8.28	169	0.290	0.329	0.507	5.02	130	4
Cano	Robinson		0.358	0.592	6.36	130	0.308	0.375	0.508	5.79	150	1
Choo	Shin-Soo		0.369	0.599	6.63	135	0.283	0.401	0.460	5.74	148	1
Davis	Rajai		0.320	0.462	4.46	91	0.255	0.299	0.369	3.40	88	2
Doumit	Ryan		0.303	0.487	4.43	90	0.241	0.310	0.388	3.68	95	1
Ellis	Mark		0.317	0.412	3.99	81	0.264	0.305	0.343	3.27	84	2
Ellsbury	Jacoby		0.350	0.499	5.28	108	0.294	0.347	0.419	4.50	116	2
Fielder	Prince		0.341	0.563	5.76	118	0.278	0.355	0.458	4.96	128	1
Fowler	Dexter		0.347	0.538	5.61	114	0.260	0.360	0.403	4.54	117	3
Freese	David		0.334	0.490	4.94	101	0.257	0.326	0.377	3.83	99	3
Ibanez	Raul		0.331	0.675	6.76	138	0.237	0.302	0.482	4.37	113	3
Infante	Omar		0.348	0.494	5.20	106	0.314	0.343	0.446	4.68	121	2
Johnson	Kelly		0.320	0.558	5.36	109	0.234	0.300	0.408	3.72	96	3
Jones	Garrett		0.310	0.560	5.21	106	0.232	0.287	0.419	3.63	94	4
Kinsler	Ian		0.301	0.443	4.02	82	0.269	0.331	0.396	4.05	105	1
Lough	David		0.332	0.481	4.82	98	0.278	0.300	0.403	3.68	95	2
McCann	Brian		0.313	0.564	5.30	108	0.255	0.328	0.459	4.57	118	1
McLouth	Nate		0.307	0.475	4.38	89	0.258	0.325	0.398	3.99	103	1
Morneau	Justin		0.315	0.494	4.68	95	0.254	0.314	0.399	3.84	99	1
Morrison	Logan		0.299	0.466	4.19	85	0.241	0.329	0.376	3.86	100	1
Murphy	David		0.252	0.427	3.23	66	0.218	0.279	0.369	3.14	81	2
Peralta	Jhonny		0.399	0.601	7.22	147	0.304	0.359	0.457	5.03	130	4
Pierzynski	AJ		0.320	0.500	4.81	98	0.272	0.287	0.425	3.68	95	2
Ruggiano	Justin		0.308	0.551	5.09	104	0.224	0.293	0.401	3.57	92	3
Saltalamacchia	Jarrod		0.407	0.695	8.49	173	0.274	0.340	0.467	4.82	125	3
Schumaker	Skip		0.313	0.396	3.80	78	0.261	0.320	0.329	3.36	87	1
Smith	Seth		0.338	0.524	5.32	109	0.251	0.323	0.388	3.88	100	3
Stubbs	Drew		0.339	0.519	5.29	108	0.228	0.300	0.349	3.25	84	3
Trumbo	Mark		0.327	0.636	6.27	128	0.231	0.292	0.449	3.94	102	3
Young	Chris		0.277	0.525	4.38	89	0.200	0.278	0.379	3.20	83	3
		MLB AVG	0.323	0.505	4.90		0.253	0.318	0.396	3.87

An editorial comment – it’s always bugged me that HR are excluded from BABIP calculations. Not all HR are created equal – a ball that goes over the fence and is excluded from BABIP for one hitter, goes into a glove and is included in BABIP – negatively – for another. End of editorial. Also, for the purposes of this exercise, the BIP and Total (including K and BB) run values are being calculated and scaled to the MLB average ERA as follows: the square of ((1.7 * Player OBP + Player SLG)/(1.7 * MLB OBP + MLB SLG)) * MLB Avg ERA. Also, I am arbitrarily excluding HBP from the above OBP calculations, and including SH and SF as ordinary outs for the purpose of this exercise. The estimated run values excluding and including the K/BB data are scaled to 100 in the 4th and 9th columns above. They are not adjusted for park factors.

Based on performance on batted balls alone, Jarrod Saltalamacchia (with plenty of help from the Green Monster) and Marlon Byrd (with some help from the surprisingly compliant Citi Field LF/LCF area) are the most productive, with the somewhat unlucky David Murphy and groundball machine Norichika Aoki bringing up the rear. The average MLB player’s run value scaled to ERA is decreased by 1.03 (from 4.90 to 3.87) by the addition of the K/BB data – the 34 players above saw their run values decrease by an average of 1.22. Now let’s look at the results by K/BB group.

	GROUP #	AVG -RUN	+/- R100
– K, + BB	1	0.49	13.6
– K, – BB	2	0.81	4.8
+ K, + BB	3	1.84	-16.2
+ K, – BB	4	2.24	-25.2

The table above shows how significantly a high K rate, especially, negatively impacts a player’s ability to achieve a high level of production. Hitters in Group 3 – higher than average K rate, higher than average BB rate – see their run value scaled to 100 fall by an average of 16.2 basis points from their run value based on batted balls alone. The Group 4 hitters – higher than average K rate, lower than average BB rate – are hit even harder, by an average of 25.2 basis points. This is the dynamic that drives a Mark Trumbo – a Group 4 player with a batted ball run value of 128, into a league average-ish 102 guy once K and BB are added in. It makes someone with an acceptable batted ball profile like JP Arencibia (Group 4 – 93) into a sub-replacement level offensive player (64). It makes someone like Marlon Byrd (Group 4 – off-the-charts 169 on batted balls) utterly reliant on such a level of batted ball authority because of the huge minus contributed by his poor K and BB rates. Conversely, players with relatively unimposing batted ball profiles like Norichika Aoki (Group 1 – 72) and Ian Kinsler (82) whose Group 1 K/BB profiles make them league average-ish offensive players with relative run values of 100 and 105 once K and BB are taken into account. In a nutshell, if you’re going to strike out a lot, you had better punish the baseball consistently when you do make contact.

The tables above give us more reasons why a hitter like Robinson Cano is as good as he is. Based on his batted ball performance alone, five hitters above rank above his relative run value mark of 130. His solid Group 1 K/BB performance bumps him way up to 150 in terms of overall relative run value, the best among this group. Cano isn’t overly reliant on impact batted ball authority, or on impact K or BB rates. He’s simply good at all of these aspects of hitting, and while each of his abilities will deteriorate over time, he’s unlikely to suffer a sudden plummet because of a breakdown in any single aspect of his game – his other skills will keep afloat longer than a player overly reliant on one single component.

It is self-evident that K and BB rates impact the production of position players and pitchers, but the above helps quantify that impact. A very similar analysis for pitchers can and will be prepared in an upcoming post. Obviously, there are many angles not taken into account here – park factors, the impact of player speed on performance on balls in play, luck, etc. – but this much is clear. Maintaining solid K and BB rates do not alone make a hitter good or bad, but doing so clearly enhances the margin for error a hitter possesses with regard to the authority with which he puts the ball in play. Ian Kinsler’s batted ball authority has declined in the recent past – but his K and BB rates have allowed him to at least continue resembling Ian Kinsler – for now.