Projections and Accuracy for hitters – a look back at 2016

By clayd On March 7, 2017 · Leave a Comment

When it comes to trying to determine who had the most accurate projections, there are a lot of things to consider. And, if you try to consider them all, you won’t have a post – you’ll have a book.

You can use straight statistics, or you can bias-adjust. You can use whole stats. Rate stats. One-on-one comparisons for individual players. All stats. Just fantasy-relevant stats.

What I’ll do, here, is a fairly straightforward comparison between the numbers projected and the results. I’ll do some comparisons with and without bias adjustments, although part of your forecast skill should include not having large biases in the first place. And, for today, we’ll limit ourselves to hitters.

I’m going to be looking at average forecast errors for multiple statistics. If you’re going to do that, you really need to make sure that you are dealing with a consistent set of players. Having someone included in stat A but not stat B’s list can distort things badly. I eventually came up with a list of 256 players to form my focus group, if you will. These 256 were people that I thought, in the 2016 pre-season, would get at least 200 PA in 2016. But not just me – they only made the list if I, and Steamer, and Pecota, and Rotowire, all agreed they would get 200 PA, and then really did. We all made forecasts for these players, and they played enough to make a reasonable grade.

For projections, I have my own, of course, two varieties – the pure computer printout (“Autoclay”), and what I get from plugging those projections into lineups and depth charts and making my own judgments of playing time (“Clay”). I took these from copies I had on my computer from April 3; the latter were the ones I used for my own fantasy league drafts, last April 2 and 3. The fellows at FantasyPros.com helped me out by sharing copies they had made for Zips and Steamer (FanGraphs) last spring. I had downloaded stats from Pecota (Baseball Prospectus) and Rotowire, and I could run a variety of stats myself.

Now, the simplest, most naive forecast you could make, would be to simply use each player’s 2015 major league performance as his 2016 projection. That yields this

Stat	PA	H	DB	TP	HR	BB	SO	R	RBI	SB	CS	BA	ISO	bbrate	sorate		Sum	rates	roto
2015 real	118.22	34.65	8.14	1.86	6.78	14.44	30.10	20.82	22.75	4.55	2.07	12.33	17.33	10.21	22.84		327.09	62.70	67.24

The numbers for PA through CS are simply the raw, average, absolute value of the differences, between forecast and reality, for all 256 players. The number for batting average shows the average difference, in hits, between projected and actual batting average, given real atbats. For example – suppose someone really went 100-for-400, a .250 batting average. The forecast was .293 – so the error is 17.2 hits (.293 minus .250, times 400). Similarly, the ISO number shows the difference in extra bases between actual and forecast isolated power. bbrate is the difference, in walks, between real and forecast BB/PA, for actual PA; so rate is the same thing for strikeouts. The “Sum” box is the simple sum of the 15 category boxes; “rates” sums just the four rate statistics; “roto” sums the R, HR, RBI, SB, and BA categories. So a 327 sum – that’s you’re naive baseline.

You can do a little better by taking 2013-15 average, instead of just 2015:

Stat	PA	H	DB	TP	HR	BB	SO	R	RBI	SB	CS	BA	ISO	bbrate	sorate		Sum	rates	roto
2013-15 real	119.13	34.55	7.78	1.49	6.41	15.01	24.65	21.37	22.49	4.26	1.90	11.54	15.40	9.77	23.50		319.27	60.21	66.08

Your next step up in sophistication is to start adding some minor league stats into the ratings. When I run a simple 2013-15 DT for each player, and use that for my forecast – this will have no park factors, no adjustments to league average, and the PA will generally be projected into a 650-PA total, so they won’t project part-time PA at all – well, that gets me this:

Stat	PA	H	DB	TP	HR	BB	SO	R	RBI	SB	CS	BA	ISO	bbrate	sorate		Sum	rates	roto
2013-15 DT	152.04	41.39	9.25	1.95	7.25	17.37	29.25	25.11	24.32	5.83	2.62	9.91	14.06	8.30	10.15		358.80	42.43	72.42

which isn’t really very good at all, thanks to the way PA are thrown off, and all the counting stats along with it. Projecting to a 650_AP season is definitely NOT the way to go. But the rates stats are dramatically better. Lets improve on the previous by taking a simple average of the three years 2013-15, with one little added wrinkle – minor league stats only count for 50% of major league stats.

Stat	PA	H	DB	TP	HR	BB	SO	R	RBI	SB	CS	BA	ISO	bbrate	sorate		Sum	rates	roto
13-15 DT+	104.98	30.73	6.74	1.40	5.95	12.99	17.96	17.64	19.78	4.08	1.88	9.85	14.13	8.29	9.90		266.28	42.17	57.29

That figure, by itself, is comparable to most of the other stats I’ll run. That value is pretty much the primary input to the autoclay – the computer printout, without any further help from me:

Stat	PA	H	DB	TP	HR	BB	SO	R	RBI	SB	CS	BA	ISO	bbrate	sorate		Sum	rates	roto
autoclay	98.70	27.64	6.41	1.52	5.94	11.37	22.47	16.04	16.38	3.81	1.50	9.37	14.43	7.34	12.80		255.72	43.94	51.53

Next was ZIPS, taking another step forward:

Stat	PA	H	DB	TP	HR	BB	SO	R	RBI	SB	CS	BA	ISO	bbrate	sorate		Sum	rates	roto
zips	94.80	26.82	6.39	1.66	6.22	11.56	22.96	16.08	16.72	3.97	1.67	8.98	15.07	8.16	11.92		252.99	44.14	51.98

Followed by Rotowire’s projections;

Stat	PA	H	DB	TP	HR	BB	SO	R	RBI	SB	CS	BA	ISO	bbrate	sorate		Sum	rates	roto
rotowire	91.30	27.20	7.04	1.65	6.08	11.68	22.34	15.84	16.83	4.37	1.83	9.51	15.19	8.11	12.73		251.69	45.54	52.63

Ahead of Rotowire is where I show up:

Stat	PA	H	DB	TP	HR	BB	SO	R	RBI	SB	CS	BA	ISO	bbrate	sorate		Sum	rates	roto
clay	91.77	26.32	6.30	1.50	6.11	11.35	22.35	15.85	16.60	3.72	1.51	9.32	14.42	7.74	12.47		247.33	43.95	51.59

I’d like to say that’s where it ends, but I have to honestly report that still leaves PECOTA

Stat	PA	H	DB	TP	HR	BB	SO	R	RBI	SB	CS	BA	ISO	bbrate	sorate		Sum	rates	roto
pecota	90.83	25.77	6.14	1.59	5.90	11.77	21.69	15.57	16.25	4.04	1.51	9.04	14.62	8.08	12.61		245.40	44.34	50.80

And, on top, Steamer:

Stat	PA	H	DB	TP	HR	BB	SO	R	RBI	SB	CS	BA	ISO	bbrate	sorate		Sum	rates	roto
steamer	88.62	25.57	6.18	1.51	6.20	10.91	21.64	15.85	16.48	3.93	1.94	8.88	14.91	7.44	12.01		242.08	43.24	51.35

The differences really aren’t that big between them. For instance, if we take the best score on a player by player basis for the 256 player group, then among the top three the results are fairly even – Clay was best on 80, Steamer on 89, Pecota on 87. Clay beats Steamer 129-127 head to head; Pecota beats Clay 133-123; and Steamer beats Pecota 132-124.

{Note – I had this virtually finished this by about Valentine’s Day, and then – right before hitting the publish key – made one more check and discovered that the “real” stats I was using for validation were not the actual, real stats. Wiping out a lot of work and several erroneous conclusions. Then things went crazy at work, and I focused on getting stats for other leagues in, and before I know it its March – not just barely, but a full week in. Time sure seems to move a lot faster than it used to. May also have to do with being more committed to my day job than I used to be – I could never work on this during the day, the way I used to.]

1/29 Stats update

By clayd On January 30, 2017 · Leave a Comment

Incremental update, as a few of players and rumors start sorting themselves out.

Kansas City – the biggest change is probably in Kansas City, where Yordano Ventura’s unfortunate death blows a 30-start hole in what was already a stretched-out rotation. Ventura’s loss does far more to hurt the team than picking up Brandon Moss helped.

Tampa Bay/Dodgers – The big Logan Forsythe trade improves the Dodgers second base position, largely pushing Enrique Hernandez out of the way, for a (very good) pitcher that I had them barely using. Jose De Leon will get a much bigger role in Tampa Bay, and he should be a solid starter for them. It opens up second base for Brad Miller; if they can pick up a good first baseman (I have speculatively given them Chris Carter now, instead of Baltimore), they are in real contention for the AL East. Even if they don’t, I currently have Matt Duffy under-utilised, trying to make space for Willy Adames and Daniel Robertson, two prospects I like a lot.

Angels – Luis Valbuena takes a big bite of CJ Cron.

Giants – Signing Nick Hundley opens up the possibility that we could see more of the Posey to first, Belt to LF we saw a couple of years ago. Their LF situation is weak, but Parker and Williamson are both better hitters than Hundley, so in strict terms it won’t help the offense. If getting Posey off his knees a little while helps him stay fresher, though, than it may be worthwhile.

Rockies – Greg Holland has unsettled the closer situation in Colorado. At this point, I’m still going to lean towards Ottavino – he’s the incumbent, and has a better projection than Holland – but the projections do get tricky with guys who have missed a full season. What was an 80/15 split between Ottavino and McGee is now a 65/30 split for Ottavino and Holland.

Braves – Kurt Suzuki is their starting catcher.

And various minor changes for the Nationals (Stephen Drew, Vance Worley), Cubs (Brett Anderson, Jim Henderson), Diamondbacks (Gregor Blanco,), Indians (Austin Jackson), White Sox (Peter Bourjos), Mets (speculatively adding Sergio Romo), Oakland (Adam Rosales and Alejandro de Aza), Phillies (Ryan Hanigan), Padres (Jered Weaver?), and Rangers (James Loney, Wesley Wrigth).

2017 Projections – First Release

By clayd On January 16, 2017 · Leave a Comment

Happy Birthday, MLK. And, thanks to the three-day weekend that comes from that, I was able to complete my first release of 2017 projections. Projections which, if 2016 is any guide, will be arguably the best projections you can find.

For those of you stumbling into this site from the Google, my name is Clay Davenport. I’ve been messing with baseball statistics since about 1980, developing my own sets of tools for run scoring and player ratings (Equivalent Average), and my own methods for translating statistics between leagues (minors, past majors, foreign leagues). I was one of the founding members of Baseball Prospectus, but split from them several years ago and have since gone my own way. And one of the things I did on my own was to go back to using my stats my way – which is what we have here. In the daytime, I work for NOAA – maintaining software and data distribution from our fleet of weather satellites. This site, as much as anything, is about allowing me to access my stats over the internet. Which is my way of explaining why I don’t write that much – this site is about the numbers, not the words.

So, about these projections. They are a two-part effort. Part number one relies on the computer algorithms. I have a database of player-seasons, most of which is displayed one way or another on this sit, which goes back to 1871 for the majors, to 1947 for AAA, and to 1978 for most other Organized leagues. I’ve run some statistics to identify typical progressions, by age or position, for players; but, like BP’s PECOTA, the forecasts are primarily driven by establishing historical analogue players and seeing how they progressed from a similar point. Those are the projections labeled as “the original computer projections”, “All 2017 Hitters” and “All 2017 Pitchers“.

Part 2 is about me, and my experience building forecasts like this. The computer projections get dropped into a monstrous excel spreadsheet, where they form the basic forecast input. Another part of that spreadsheet contains depth charts for every team – into which I allocate my opinion of who plays where and how much. Flipping to the White Sox page, I see I have Jose Abreu down for 75% of the playing time at first, with another 15% coming at DH. That puts him in at 90% total playing time, and 617 projected PA, slightly more than the 602 he gets from the computer. I gave Todd Frazier gets 20% of the 1B time – a lot of that is driven by trying to guess how they’ll work Yoan Moncada in, which leads to shuffling Frazier and Lawrie around. But those numbers will refine as spring training and then the regular season comes around, and I will be updating steadily. So this is primarily the computer projections for rates, with my, major league only, PT estimates superimposed. They do have one further feature – all the statistics are adjusted so that the total statistics of this set match the total statistics of 2016. This means that my projections will balance – runs scored equals runs allowed, hitter homers equal pitcher homes, et cetera. These, then, are the stats in the Major League Hitters and Major League Pitchers links.

As for the projected standings – that’s the way things fall out when the projections are applied. There is big love for the Cubs (even more than last year, when I projected 101 for them) and for the Astros. There is big hate for the Padres and, once again, the Royals (I don’t deliberately target KC, honest; there is a mismatch between KC and the projections that was worth 10 games in 2014, 12 in 2015, and 11 last year). There are players who seem to repeatedly over- or under-perform their projections, and the Royals seem to load up on those over-performers.

In addition, I’ve done just a little bit of cheating on a couple of players. I’ve matched a couple of free agents – Bautista, Trumbo, Napoli – with their rumor mill teams, even though they haven’t actually signed. typically, these are teams with an obvious hole at those positions – we can be pertty sure they are going to sign somebody to fill the void, even if those somebodies eventually get shifted around. Anticipating that Trumbo will eventually re-sign with the Orioles is no different, really, than a forecast expecting him to hit for a .275 EQA. Again, as reality settles in, those will adjust.

So the first projection for playoffs: Astros and Indians win their division; Seattle and Texas are wild cards; Boston and Toronto in a playoff for the east. Favor Boston. Nationals, Cubs, and Dodgers win their divisions; the Marlins take one wild card; the Mets and Pirates play for the other.

Clay

By clayd On September 25, 2016 · Leave a Comment

http://www.claydavenport.com/dt/fernajo02-pitching.shtml

JOSE FERNANDEZ

Born 7-31-1992

Bats R Throws R

Actual Pitching Statistics

NAME	Hnd	AGE	YEAR	TEAM	W	L	ERA	G	GS	TBF	IP	H	R	ER	HR	BB	SO	HBP	IBB	WP	BK
JOSE FERNANDEZ	R	20	2013	FLA-N	12	6	2.19	28	28	681	172.7	111	47	42	10	58	187	5	5	3	1
JOSE FERNANDEZ	R	21	2014	FLA-N	4	2	2.44	8	8	205	51.7	36	19	14	4	13	70	0	1	2	1
JOSE FERNANDEZ	R	22	2015	FLA-N	6	1	2.92	11	11	265	64.7	61	21	21	4	14	79	2	0	2	0
JOSE FERNANDEZ	R	23	2016	FLA-N	16	8	2.86	29	29	737	182.3	149	63	58	13	55	253	6	6	9	1
					38	17	2.58	76	76	1888	471.3	357	150	135	31	140	589	13	12	16	3

Advanced Pitching Statistics

NAME	Hnd	AGE	YEAR	TEAM	IP	RA	DH	DR	DW	XIP	NRA	RAA	PRAR	PRAA	DERA	XIP	NRA	RAA	PRAR	PRAA	DERA	STF
JOSE FERNANDEZ	R	20	2013	FLA-N	172.7	2.45	-22	4	1	161.5	2.77	31	52	33	2.65	183.7	2.78	35	58	37	2.70	32
JOSE FERNANDEZ	R	21	2014	FLA-N	51.7	3.31	-5	5	1	50.6	3.78	4	12	6	3.40	58.0	3.85	4	13	7	3.48	43
JOSE FERNANDEZ	R	22	2015	FLA-N	64.7	2.92	8	-4	2	60.9	3.25	8	16	9	3.17	69.4	3.23	10	18	10	3.20	39
JOSE FERNANDEZ	R	23	2016	FLA-N	182.3	3.11	12	-1	1	187.5	3.36	24	50	28	3.15	218.4	3.41	26	58	32	3.19	45
					471.3	2.86	-6	4	5	460.5	3.26	67	131	76	3.01	529.5	3.22	76	148	85	3.05

Translated Pitching Statistics

Name	Age	Year	Team	IP	H	ER	HR	BB	HBP	K	ERA	W	L	H/9	HR/9	BB/9	K/9
JOSE FERNANDEZ	20	2013	FLA-N	196.3	134	59	14	65	5	183	2.70	15	6	6.1	.6	3.0	8.4
JOSE FERNANDEZ	21	2014	FLA-N	59.3	41	23	5	15	0	67	3.49	4	3	6.2	.8	2.3	10.2
JOSE FERNANDEZ	22	2015	FLA-N	73.7	76	26	6	16	2	71	3.18	5	3	9.3	.7	2.0	8.7
JOSE FERNANDEZ	23	2016	FLA-N	212.3	189	75	16	59	6	233	3.18	17	9	8.0	.7	2.5	9.9
				541.7	440	183	41	155	13	554	3.04	41	21	7.3	.7	2.6	9.2

General Pitching Splits

					ALL			HOME			ROAD			VS	LH		VS	RH		BASES	EMPTY		MEN	ON	BASE	START	PIT		RLF	PIT
name	Hnd	Age	Year	Team	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR
JOSE FERNANDEZ	R	20	2013	FLA-N	681	.195	43	375	.161	15	306	.230	28	361	.206	25	320	.181	17	431	.187	24	250	.207	18	681	.195	43	—	—	—
JOSE FERNANDEZ	R	21	2014	FLA-N	206	.196	13	133	.144	4	73	.265	9	106	.250	12	100	.116	2	147	.089	2	59	.336	12	206	.196	13	—	—	—
JOSE FERNANDEZ	R	22	2015	FLA-N	265	.239	26	141	.240	14	124	.238	12	122	.310	20	143	.163	6	157	.226	14	108	.257	12	265	.239	26	—	—	—
JOSE FERNANDEZ	R	23	2016	FLA-N	737	.227	65	408	.195	26	329	.260	38	401	.252	44	336	.195	21	439	.209	32	298	.250	33	737	.227	65	—	—	—
					1889	.214	148	1057	.185	60	832	.246	87	990	.244	101	899	.178	47	1174	.193	72	715	.245	76	1889	.214	148	—	—	–

Pitcher Splits by Month

					ALL			MAR	APR		MAY			JUNE			JULY			AUG			SEPT	OCT
name	Hnd	Age	Year	Team	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR
JOSE FERNANDEZ	R	20	2013	FLA-N	681	.195	43	102	.247	11	120	.247	13	125	.143	4	131	.195	8	151	.151	5	52	.158	2
JOSE FERNANDEZ	R	21	2014	FLA-N	206	.196	13	153	.170	7	53	.256	6	—	—	—	—	—	—	—	—	—	—	—	—
JOSE FERNANDEZ	R	22	2015	FLA-N	265	.239	26	—	—	—	—	—	—	—	—	—	127	.219	11	44	.179	2	94	.286	14
JOSE FERNANDEZ	R	23	2016	FLA-N	737	.227	65	119	.236	11	148	.203	10	100	.133	3	133	.261	16	126	.254	14	111	.242	11
					1889	.214	148	374	.214	29	321	.229	30	225	.139	6	391	.227	35	321	.200	21	257	.245	27

Pitcher Splits by Count

					0	–	0	0	–	1	0	–	2	1	–	0	1	–	1	1	–	2	2	–	0	2	–	1	2	–	2	3	–	0	3	–	1	3	–	2			UNKNOWN
name	Hnd	Age	Year	Team	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR	PA	EQA	EQR
JOSE FERNANDEZ	R	20	2013	FLA-N	80	.356	18	57	.242	6	88	-.191	-7	44	.220	4	50	.179	3	119	-.114	-2	10	.110	0	27	.116	1	91	-.139	-3	15	.999	5	27	.568	7	73	.333	12	—	—	—
JOSE FERNANDEZ	R	21	2014	FLA-N	26	.284	4	15	.379	4	29	-.165	-2	10	.245	1	11	.322	2	43	-.144	-2	3	.450	1	7	.269	1	35	-.148	-1	4	.999	1	5	.237	0	18	.325	3	—	—	—
JOSE FERNANDEZ	R	22	2015	FLA-N	25	.273	4	24	.393	6	39	.126	1	18	.345	4	22	.225	2	56	-.170	-3	6	.623	3	8	-.142	0	28	.100	0	1	.999	0	9	.547	3	29	.433	7	—	—	—
JOSE FERNANDEZ	R	23	2016	FLA-N	67	.348	15	59	.333	12	91	-.136	-3	37	.398	11	38	.262	5	147	.107	2	22	.295	3	23	.153	1	108	-.111	-2	14	.999	4	18	.537	5	113	.266	13	—	—	—
					198	.333	40	155	.314	28	247	-.152	-10	109	.308	20	121	.230	12	365	-.097	-5	41	.330	8	65	.142	2	262	-.121	-6	34	Inf	11	59	.517	16	233	.313	35	—	—	–

Park factor updates for early May

By clayd On May 8, 2016 · Leave a Comment

Updated park factors for all teams through the first 30 days of the season.

I use a weighted mean park factor for all teams – a five year moving average, weighted 3-2-1 for the one-year factors. So the park factors for 2010 use (3*2010PF + 2*2011PF + 2*2009PF + 2008PF + 2012PF)/9, assuming they are in the same park throughout.

For the current season I obviously cannot do that – I don’t know what the 2017 and 18 factor s will be – so it reduces to a 3-2-1 average of 2016, 2015, and 2014. However, it is also way too early to take the 2016 factors at face value. At this pont, one month, or about 1/6 of the season in, I’m going to use (1/6) the current season to date, plus 5/6 of last year’s weighted value. So, Arizona, who had a 1020 in 2015, a 1065 in 2014, and 971 in 2013 had a weighted average of 1027 coming into this season. That’s their baseline. They have an 1168 PF through the first month, which means I am giving them a rating of 1051 for the current season.

There are a couple of possibly random, possibly not, geographic relations in there, as weather patterns do play into the park factors:

1) The mid-Atlantic teams, Washington, Philadelphia, and Baltimore, are all strongly below average so far (-110, -111, and -100 below expectations). Most of the nearby minor league parks are as well – like Bowie (-109), Delmarva (-132), Wilmington (-108), and Potomac (-65).

2) The teams further into the northeast – Boston, Toronto, the Mets and Yankees – are all above average (+44, +61, +42, and +140). Again, most minors follow: Pawtucket is running at +152, Trenton at +62, Syracuse +88, Rochester +51, Scranton +49, New Hampshire +98, Binghamton +33, and Lakewood +202.

3) The southwest teams – Arizona, Colorado, and Texas– are well above average (+141, +90, +86). Here, though, the minor league teams do not match – Albuquerque is at -145, Colorado Springs -101, Las Vegas -65.

4) Both Chicagos are down (-54 Sox, -18 Cubs), as are the Brewers (-60), Cardinals (-91), and Twins (-106). The minors are mixed: Indianapolis (-65) fits, as does Kane County (-173) and Quad Cities (-60), but other nearby Midwest league teams do not (South Bend -10, Wisconsin +39, Peoria +78, Beloit +26), and neither does Iowa (+12).

5) Both Florida teams are down (TB -113, Miami -58). The FSL teams are useless for this comparison; Jacksonville and Pensacola are both fairly positive (+62, +52), more in common with Atlanta (+65).

6) The Tigers are +31, with the Indians (-2), Pirates (+1), and Reds (-25), forming a fairly neutral block. Erie (-1) and Altoona (-15), but the other Ohio teams are strongly negative: Columbus -89, Akron -152, Lake County -57, and Dayton -68.

7) The West Coast teams are all over the place: -1 in Seattle, +51 and -47 for the Giants and As, +36 and -93 for the Angels and Dodgers, +1 for the Padres.

8) And three as yet unmentioned: Kansas City (-2), Houston (-6), and Atlanta (+65) who tend to be in between the other teams mentioned.

Welcome to the Majors, Jeremy Hazelbaker

By clayd On April 4, 2016 · Leave a Comment

Debut: April 3, 2016. Pinch-hit for Adam Wainwright in the top of the 7th; struck out.

Card(s): minors, majors

Hazelbaker is a 28-year-old rookie, who parlayed a .304/.373/.543 (.334 EqA) spring performance into a spot on the Opening Day roster (thanks also in part to a late injury to Ruben Tejada). He’s a corner outfielder, despite some fairly impressive speed. He was a fourth-round pick from Ball State in 2009, to the Red Sox, got traded to the Dodgers in the 13/14 offseason. They released him last May, and the Cardinals picked him up.

In the Red Sox system, he steadily worked his way up to AAA Pawtucket, generally hitting for a .250ish EQA; he showed slightly above average power (+5 POW component), good speed (+10), poor strike zone control (-14 K, 0 W). As a Dodger, he was awful – a .184 EqA in Albuquerque, then sent down to hit .213 in AA Chattanooga, and opened 2015 by hitting .222 in AA Tulsa…hence the release.

As a Cardinal, though, a whole new life. His combined DT for Springfield and Memphis last year comes to .282/.350/.497, .294 EqA, and the spring works out to a .320-ish EqA. The most significant change from his past is cutting his strikeouts down (from 24% of PA and a -14 K component in 10-13 to 20% and -7 in 15-16), while raising his BABIP component from -1 to +9.

It is possible that some latent skills have been unlocked through skillful coaching, and that the Cards have found lightning in a bottle. Its hard to imagine him breaking through against a pretty set outfield. If the Cards decide to let go of Brandon Moss – a move which would set off dominoes that push Holliday to first, Grichuk to left, and Pham to center with some regularity – then he is well positioned to hold down a 4th OF slot for the season.

Spring stats are up!

By clayd On March 11, 2016 · Leave a Comment

Got the code running for spring training statistics last night.

Baselines
Cactus 5.30 R/G, cure .279/.347/.451, 3.19 BB and 7.36 K/9 inn
Grapefruit 4.83, .267/.340/.418, 3.43 and 7.20

Top EqRs so far, with 6:
Bryan Holaday, Detroit, .631 eqa/6 eqr
Scott Sizemore, Washington, .571/6
Mike Trout, LA Angles, .519/6
Avisail Garcia, Chic White Sox, .438/6
Scott Van Slyke, LA Dodgers, .437/6
Maikel Franco, Philadelphia, .397/6

No pitcher has thrown more than 7 innings yet, so no real separation from the pack.

Projection updates 2/8/16

By clayd On February 9, 2016 · Leave a Comment

Changes since last weekend:

Chase Anderson traded from Arizona to Milwaukee
Joins a Brewer rotation with nine potential starters and it isn’t completely
obvious who the best 1 is, let alone the best 5. FWIW, he rates as the 3rd best
projection, and should keep a regular slot in the Brewer’s rotation this year.

Anthony Bass Free agent, going to Japan
Marginal bullpen guy; played parts of five season in the majors, but always
had at least a little minor league time as well.

Jesse Biddle traded from Philadelphia to Pittsburgh
Didn’t have him projected to pitch for Philadelphia this year, so that
certainly won’t change with a tougher Pittsburgh lineup.

Greg Bird injured
Shades of Nick Johnson. He figured to get a lot of playing time in New York,
thanks to the fragility of Teixeira and Rodriguez. As I try to work out the
playing time, it seems to me like Aaron Hicks may be a beneficiary – shifting
Ackley to first and Beltran to more DH will open outfield time.

Jeremy Bleich free agent signed by Philadelphia
Does not project to make the team.

Andrew Brown free agent signed by Angels
28 HR season for Korea’s SK Wyverns last season, but in translation it is
virtually identical to his 2014 Las Vegas season – the one that left him in a
position where he has to go to Korea to find a job. The projection at .252 is
surprisingly optimistic, and the Angels’ outfield depth chart beyond Trout and
Calhoun is pretty bad, so there could be an opening for him at some point of
the season.

Darrell Ceciliani traded from Mets to Toronto
There are a lot of contenders for outfield spots in Toronto, and Ceciliani’s
projected 1.3 WARP is only sixth best on the team. He’s got a good chance at
playing sometime this season, depending on injuries, but it would take a lot
to get him into a lead role.

Maikel Cleto free agent signed by White Sox
Slots as somewhere around the 12th option for the Sox bullpen, which puts him
on the border of zero innings.

Tyler Cloyd free agent signed by Yankees
Shouldn’t crack the team.

Louis Coleman released by Kansas City
Middle of the pack option; should get picked up somewhere.

Jean Cosme traded from Baltimore to San Diego
Only pitched at Aberdeen (short-season A) in 2015, so years away – if ever.

Odrisamer Despaigne traded from San Diego to Baltimore
Not just moving from the NL to the AL, but from spacious Petco to cozy
Camden Yards. It won’t be pretty.

Isan Diaz traded from Arizone to Milwaukee
The 2015 Pioneer League MVP, he hit for a .333 EqA in Missoula (.276
translated). There’s not a whole lot of corelation between hitting .330 at
age 19 in rookie ball and having a major league career – for every Manny
Ramirez and Jim Thome (#1 and #2 in PIO/APL EQA at age 19) there’s a Mike
Restovich and Jesus Cota (#3 and #4). He’s got a few big comps (Corey Seager,
Darryl Strawberry) and a lot of nobodies. Intriguing but far from guaranteed.

Chris Dwyer free agent signed by Baltimore
No impact.

Gavin Floyd free agent rumored to sign with Toronto
Not likely to be able to contribute much, but could be the key to keeping
Roberto Hernandez from appearing on the mound.

Christian Friedrich waived from Colorado to Angels
Goes from the middle of the Rockie pen to much tougher competition – I can’t
even give him any innings now.

Yovani Gallardo free agent rumored to sign with Baltimore
If its true, it would help immensely as an innings-eater, and pushes away
innings I’d previously allocated to Vance Worley and Mike Wright; with
Gallardo they can go back to being a very ocasional starter.

Conor Gillaspie free agent signed by San Francisco
Disastrous 2015 was well below his established standards. Assuming he can
return to those prior standards, he makes a better insurance policy for Matt
Duffy than anyone else on the Giants’ roster. He could be a regular
backup/pinch hitter.

Jonny Gomes gone to Japan
Offensive projections are down to barely replacement level, defensively
challenged and platoon-limited.

Raywilly Gomez free agent signed by Mets
Catcher with a reasonably good bat, but yet to play above AA. Could
potentially get called up if injuries hit the Mets’ leading catchers, but
those roles usually goes to defensively solid guys. That doesn’t describe
Raywilly.

Angelo Gumbs free agent signed by Reds
Half of his top 20 comps did not play anymore.

Aaron Hill traded from Arizona to Milwaukee
Hill was expected to be a utility IF in Arizona, and could well be the same in
Milwaukee. One obvious ploy is as a platoon partner for Gennett at second. He
could also be a semi-regular at third base, and could arguably be the best 3B
option if the team was in win now mode. Since they’re not, expect him to be
there as the Brewers cast and reject multiple auditions.

Edgar Ibarra free agent signed by Philadelphia
Pretty generic bullpen arm. He is lefty, albeit without a particularly large
split. I could see him getting called up for a few weeks.

Don Kelly free agent signed by Miami
Had Tommy John surgery on his throwing arm last July. Since he can play
everywhere, and he’s suitably grizzled, he might stick around, but I’m not
slotting him in anywhere just yet.

Blake Lalli free agent signed by Atlanta
If the Braves need a sixth catcher in 2016, he’s their man.

Dae Ho Lee free agent signed by Seattle
A big, big guy – my card says 6’4″, 220, but apparently more like 300 now –
who strated in Korea but played the last four years in Japan. He led the JPL
in EqR in his first year there, 2013, and finished 5th each of the last three
years. His projection won’t put him ahead of either Adam Lind or Jesus
Montero, who I currently have penciled in for 1B and DH, but Montero has yet
to demonstrate that he can meet a projection. I’d call this a legitimate
player battle.

Steven Lerud free agent signed by Seattle
Replaces Jesus Sucre and his broken leg as the backup catcher in Tacoma.

Tyler Massey free agent signed by Colorado
Below replacement, won’t matter in Denver.

Yoervis Medina traded from Pittsburgh to Philadelphia
Had him down for a token 10 ip in Pitt. In Philadelphia, he projects as one of
their better pitchers – more of an indictment of them than an endorsement of
him, OK?

Jared Mitchell free agent signed by Yankees
Deep depth.

Jonathan Mota free agent signed by Cubs
Not happening.

Matt Murton free agent signed by Cubs
He’s going to try to returns to the Cubs after six years in Japan. Four of
those six were vintage Murton – high contact and average, no power, speed, or
walks. The other two, which includes 2015, were sub-par. I don’t really see a
path to playinmg time for him, not do I expect much from him if he finds one.
I’ll give him a token 5% PH role.

Hideki Okajima free agent signed by Baltimore
Certainly a blast from the past. Had a solid Japanese season in ’14, but only
had 10 miserabe appearances last year. He could find a spot in the back end of
the bullpen – but probably won’t.

Miguel Olivo free agent signed by San Francisco
I suppose Tijuana was a reasonable place for a guy with an Evander Holifield
act. Generally, these “give the old guy who used to play another spring”
signings go to guys who were good citizens in their playing days; on the other
hand, you can never have enough catchers in spring training.

Carlos Quentin free agent signed by Minnesota
Like Olivo, without the ear-biting. Quentin isn’t really that old (34 in
August), but its a brittle 34. Maybe his knees are better after a year off, or
maybe he’s just forgotten how much they hurt when stressed. I’d certainly call
Park the better option today, and maybe Max Kepler and Kennys Vargas as well.
I’ll give him a 5% though.

Esmil Rogers gone to Korea
Marginal anyway.

Deibinson Romero free agent signed by Yankees
A third baseman by trade, Romero spent most of 2015 in Korea and was terrible.
I think the .259 projection is way optimistic – the projection scheme for BA tends to
average out guys who are consistently way below average, whcih applies here.
The Yankees don’t have a real 3B behind Headley – I expect to see a SS or 2B
there when Chase needs a day off, so it makes some organizational sense.

Vinny Rottino free agent signed by White Sox
Negative projected WARP; very unlikely to see a major league park.

Brendan Ryan free agent signed by Washington
Most zombie movies seem to think that you have to shoot them in the head in order to
stop them. For baseball zombies, though, its gotta be the glove. The Nationals
have now picked up ex-New York infielders Daniel Murphy, Stephen Drew, and
Ryan – maybe they’ll try to lure Derek Jeter out of retirement?

Jean Segura traded from Milwaukee to Arizona
Presumably the starter at shortstop, even though I don’t rate him that much
higher than Owings or Ahmed. Or 2B Gosselin, for that matter.

Carlos Torres released by Mets
Kind of surprising, but bullpens are a whole lot of mix and match.

Ronald Torreyes waived from Angels to Yankees
I did have him for a cup of coffee with the Angels, trying to avoid giving
Cliff Pennington more time. I’ll do the same with the Yankees, dislodging time
that was Pete Kozma’s.

Tyler Wagner traded from Milwaukee to Arizona
A pretty good minor league pitcher the last two years, with a few caveats.
He’s been old for his leagues (High-A at 23, AA 24); and he’s scored very high
in what I call the luck components, for Batting Average and Delta Runs, which
isn’t likely to continue. He’ll still be one of the higher-ranked arms in the
organization outside Phoenix, which puts him in good shape for 4-10 starts
during the season.

First Cut – Projections for 2016

By clayd On January 19, 2016 · Leave a Comment

My first run (that I’m willing to talk about) of projections for the coming season is now up on the 2016 Projected Standings tab. They have also been used to create a new Playoff Chances Report. And, viagra order of course, site the individual projections that go into are available, again on the Projected Standings page.

AL East	Won	Lost	Runs	Runs Allowed	Champ	WC	Playoff
Blue_Jays	88	74	775	706	32.8	20.5	53.2
Red_Sox	87	75	747	690	27.4	20.1	47.5
Rays	84	78	691	660	18.7	17.4	36.1
Yankees	84	78	714	681	17.9	17.1	34.9
Orioles	76	86	684	731	3.3	4.9	8.2

AL Central	Won	Lost	Runs	Runs Allowed	Champ	WC	Playoff
Indians	88	74	741	677	49.6	13.1	62.7
White_Sox	82	80	675	668	19.0	12.1	31.0
Twins	81	81	731	731	18.1	12.0	30.1
Tigers	77	85	692	731	8.5	6.7	15.2
Royals	74	88	658	720	4.8	4.1	8.8

AL West	Won	Lost	Runs	Runs Allowed	Champ	WC	Playoff
Astros	91	71	758	662	49.5	19.0	68.6
Mariners	87	75	702	650	26.3	21.2	47.5
Angels	81	81	681	677	10.1	12.7	22.7
Athletics	80	82	676	684	7.8	10.4	18.1
Rangers	79	83	725	748	6.3	8.8	15.1

NL East	Won	Lost	Runs	Runs Allowed	Champ	WC	Playoff
Nationals	89	73	697	630	51.5	22.5	74.0
Mets	87	75	653	605	40.1	25.7	65.8
Marlins	77	85	654	693	7.5	11.7	19.2
Phillies	65	97	589	731	0.5	1.1	1.6
Braves	64	98	587	735	0.5	0.9	1.3

NL Central	Won	Lost	Runs	Runs Allowed	Champ	WC	Playoff
Cubs	95	67	747	622	70.9	14.8	85.7
Pirates	83	79	685	667	13.9	23.2	37.1
Cardinals	82	80	672	662	12.2	21.5	33.7
Brewers	73	89	667	741	1.9	4.8	6.7
Reds	71	91	646	740	1.1	3.1	4.3

NL West	Won	Lost	Runs	Runs Allowed	Champ	WC	Playoff
Dodgers	94	68	728	613	65.1	18.8	84.0
Giants	87	75	666	612	26.2	30.7	56.9
Diamondbacks	78	84	684	714	4.9	11.1	16.0
Rockies	74	88	741	809	2.4	6.3	8.7
Padres	72	90	581	656	1.4	3.8	5.1

To build these projections, I:

1) Run a computerized projection scheme, using the last three years of player performance compared against a database of all players’ four year performances. The algorithm attempts to find the most similar players, in terms of age, position, build, and performance, and the top 20 players are noted on the individual player cards.

2) Take those performances, and enter them into a very large spreadsheet, where I fill in expected playing times for all of the players. Every team, every position has to equal 100%. There have to be 162 pitching starts. Generally speaking, a) no position player gets more than 90%, and pitchers are mostly capped at 32 starts; b) rookie starters don’t get more than 80%; c) players I don’t think can hold the job all year certainly get less; d) the playing time estimates from the computer tend to carry a lot of weight. I normally set a sure starter to the 5% playing time level that first passes their projected PA, while innings are usually held under the computer’s values.

All of the statistics in the spreadsheet get rebalanced and weighted. Players on teams with high OBAs will get more plate appearances. Defense trickles back into pitchers hits (and runs) allowed. The league as a whole has to come out equal to the league totals of last year.

Current free agents won’t show up here – no team, no projected playing time. Their projections are still available on the “All hitters” and “All pitchers” downloads.

Getting to some of the players takes a deep depth chart. I’ve prepared some that you can find under the “Current Team DTs” tab. Every team has three files in there. One is a dt file, which contains the translated statistics, 2012-15, with the computer-only 2016 projection, for all hitters in that team’s system; another is a pdt file, which does the same for pitchers. The “orgdt” file just has the 2016 projections for all players on the team, sorted by position and projected WARP, like the one here for the Nationals. Kind of works as a very deep depth chart for all teams, although I can’t swear that aren’t players showing up on the wrong team (especially for players who have been released – there’s a decent chance they still show up for their old teams). That’s just for these depth charts – I am reasonably certain that every player used in the major league projections is actually a member of their team.

Coming to America (from Korea)

By clayd On January 15, 2016 · Leave a Comment

In 2015, Jung-ho Kang became the first hitter to jump directly from the Korean Baseball Organization (KBO) to the major leagues. This year, two more hitters – Byung-ho Park, for Minnesota, and Hyun-Soo Kim for Baltimore – will attempt to make the same trip.

Here’s a a draft that I worked on last January but never completed

***************************************************

Pittsburgh’s recent signing of Jeong-Ho Kang – or Jung, or Jeung, transliterations vary – got me into taking a hard look at the KBO in order to make a set of translations.

Kang’s real statistics have been very impressive, especially in two of the last three years.

Jeong-ho Kang                  Born 19870405 Age 28   Bats R   Throws R  Height 72  Weight 180   No DT, Real
Year Team         Lge  AB  H   DB  TP  HR  BB  SO  R  RBI  SB  CS  Out  BA   OBP  SLG   EqA EqR POW SPD KRt WRt BIP Defense
2006 Hyundai_Unic KBO  20   3   1   0   0   0   8   1   1   0   1   19 .150 .150 .200  .063   0  -4   7 -37 -14 -11   5-DH   0
2007 Hyundai_Unic KBO  15   2   0   0   0   0   5   0   0   0   0   13 .133 .133 .133  .000   0 -11  -6 -30 -15 -25   4-DH   0
2008 Woori_Heroes KBO 362  98  18   1   8  31  65  36  47   3   1  275 .271 .334 .392  .264  49   5  -3  -2  -3   2  96-DH   0
2009 Woori_Heroes KBO 476 136  33   2  23  45  81  73  81   3   2  355 .286 .349 .508  .276  71  14  -2   3  -3  -3 127-DH   0
2010 Nexen_Heroes KBO 449 135  30   2  12  61  87  60  58   2   2  321 .301 .392 .457  .287  71   6  -3   1   3  11 123-DH   0
2011 Nexen_Heroes KBO 444 125  22   2   9  43  62  53  63   4   6  333 .282 .353 .401  .265  60   1  -3   9  -2   0 119-DH   0
2012 Nexen_Heroes KBO 436 137  32   0  25  71  78  77  82  21   5  310 .314 .413 .560  .336 101  22   2   3   7   8 122-DH   0
2013 Nexen_Heroes KBO 450 131  21   1  22  68 109  67  96  15   8  335 .291 .387 .489  .293  78  18   0  -6   5   6 125-DH   0
2014 Nexen_Heroes KBO 415 147  36   2  39  67 106 102 115   3   3  273 .354 .458 .733  .345  96  51  -3  -9   6  19 117-DH   0
                  --------------------------------------------------------------------------------------------------------
         Minors       561 167  35   2  25  71 110  86  99   9   5 2234 .298 .382 .502  .294  96  17  -2   0   2   6

The .345 EqA in 2014 was good enough to lead the league; the .336 in 2012 earned him a second-best mark. He’s certainly in the mix in trying to figure out who the best hitter in Korea has been over the last three seasons – I would narrow it down to these four:

                  EQA   EQR
Tae-Kyun Kim     .333   250 (1st Eqa 2012, 4th 2013 and 2014)
Byung-ho Park    .331   309 (4th in Eqa in 2012, 1st 2013, 2nd 2014)
Jeong-ho Kang    .324   275 (2nd in EqA 2012, 11th 2013, 1st 2014)
Sok-min Park     .322   245  (5th in 2012, 3rd 2013)

Kang is the second-youngest of them (Byung-ho Park is 9 months younger), and, rather crucially, is a shortstop. (You can ignore the DH numbers on his stat lines; that’s just a default when no fielding data is available).

Kang’s power explosion in 2014 is not all him. The Korean league’s offensive levels have gyrated around quite a bit over the last five years, spanning a range over 1.5 runs per game:

	BA	OBP	SLG	OPS	R/G
2010	0.269	0.353	0.404	0.757	5.07
2011	0.265	0.347	0.384	0.730	4.54
2012	0.258	0.337	0.364	0.701	4.12
2013	0.268	0.353	0.388	0.740	4.64
2014	0.289	0.368	0.442	0.810	5.64

The National League, by way of comaprison, has ranged from 4.45 RPG in 2010 down to 3.95 last year. The 1930 NL – when Bill Terry hit .401, and Hack WIlson had 191 RBI – that league had a very comparable 5.68 RPG, the only NL season since 1900 over 5.5. It is a crazy high hitting environment. So you know, right away, before I even start to talk about the difficulty level of the Korean league, that those numbers are going to have to come down.

Part of the reason for that is expansion. The KBO started in 1982 with six teams, gained a seventh team in 1986 and an eighth in 1990. A ninth team was added in 2013, and a tenth will join in 2015. As generally happens when league expands, there was an immediate quality drop – the year-to-year comparisons, which had generally been improving by abou 1.5% per year, instead dropped 1.2% in 2013. League officials tried to compensate in 2014 by increasing the number of allowed foreign players from two per team to three. As a direct result of that, there were 10 players with US playing experience who made their debut in Korea in 2014, and they put up the following stat lines:

                      Year Team  Lge  AB  H   DB  TP  HR  BB  SO  R  RBI  SB  CS  Out  BA   OBP  SLG   EqA EqR POW SPD KRt WRt BIP Defense
Josh_Bell            27 2014 Twn KBO 240  64   8   1  10  30  56  33  39   3   0  180 .267 .345 .433  .258  30  10  -4  -8   2  -7  65-DH   0
Jorge_Cantu          32 2014 Bea KBO 373 116  25   1  18  34  83  56  72   1   0  259 .311 .378 .528  .282  55  19  -5  -6  -2   8  98-DH   0
Luis_Jimenez         32 2014 Gia KBO 260  82  15   0  14  41  57  42  61   0   2  181 .315 .408 .535  .299  44  22  -7  -4   6   8  72-DH   0
Yamaico_Navarro      26 2014 Lio KBO 497 154  27   1  31  96  70 118  98  25   9  357 .310 .419 .555  .302  89  19   5  11  10  -7 141-DH   0
Felix_Pie            29 2014 Eag KBO 445 145  33   2  17  41  60  61  92   9  11  323 .326 .373 .524  .276  65   8  -1  10  -2   4 117-DH   0
Brett_Pill           29 2014 Tig KBO 354 110  26   0  18  21  64  60  65  10   1  247 .311 .353 .537  .275  49  16   3   0  -7   1  89-DH   0
Vinny_Rottino        34 2014 Her KBO 215  65  18   0   1  29  22  29  21   2   0  151 .302 .386 .400  .255  25  -9  -2  17   3   0  58-DH   0
Luke_Scott           36 2014 Wyv KBO 105  28   7   0   6  20  18  17  17   0   0   79 .267 .392 .505  .284  17  21  -6   6  10 -18  31-DH   0
Brad_Snyder          32 2014 Twn KBO  97  21   7   0   4   7  31  17  17   0   2   79 .216 .287 .412  .219   9  20   4 -26  -5 -17  25-DH   0
Eric_Thames          27 2014 Dno KBO 440 151  29   6  37  57  99  95 121  11   2  297 .343 .422 .689  .329  92  37   1  -6   3  12 120-DH   0

Remember, in an unadjusted EQA the league average is always going to be .260, by definition. So everybody (with the exception of Brad Snyder, who wasn’t in Korea to start the season; he only went overseas after the Rangers released him in June) was at least in the neighborhood of average. Eric Thames was the only player who really filled a starring role, though, finishing second in the league in EQA and 3rd in EQR.

We can take a similar approach on other players who have played in Korea and the US prior to 2014. These are all players who went to korea after having played the prior three years in the US major or minor leagues – not Japan, not Mexico, no missing years. That nets us Cory Aldridge (Korea 2011), Mike Cervenak (2006), Hee Choi (2007-2009, compared with US 2004-06), Doug Clark (2008-10), Jacob Cruz (2007-08), Victor Diaz (2009), John Gall (2006), Ryan Garko (2011), Bryan Myrow (2006), Calvin Pickering (2006), Scott Seabol (2006), and Wilson Valdez (2008).

I’m going to use the player’s previous three years of translated data in the US as their “established value”. Here’s how that looks in a plot:

It is a moderately good fit, although it is distorted by five really low outliers at the bottom center of the picture. Those five belong to

Ryan Garko – while his three-year established value was .244, that consisted of a .269 three years pre-Korea, .265 two years prior, and .190 the year before. His performance in Korea was entirely consistent with someone whose real value is around .200.

Brad Snyder – who washed out of the US in June and came to Korea in mid-season.

Scott Seabol – who washed out of the US in June and came to Korea in mid-season.

John Gall – who washed out of the US in July and came to Korea in mid-season.

Mike Cervenak – who washed out of Korea and came back to the US in June.

Removing those players from the equation leaves us with this better looking graph:

**********************************************************************************************

That’s where I left off. Nothing above really needs to change. We can update the chart with league hitting stats:

Year     BA	OBP	SLG	OPS	R/G
2010	0.269	0.353	0.404	0.757	5.07
2011	0.265	0.347	0.384	0.730	4.54
2012	0.258	0.337	0.364	0.701	4.12
2013	0.268	0.353	0.388	0.740	4.64
2014	0.289	0.368	0.442	0.810	5.64
2015    0.280   0.360   0.431   0.791   5.22

And see that offense remains high in Korea. However, the allowance of more foreign players was very successful in raising 
the quality of the league - as I measure it, league strength was up almost 5% from 2013-14 and held steady in 2015 despite
further expansion.

As I noted above, Byung-ho Park, the new Twin, was one of two players I rated as a better hitter than Kang last year, and he
continued to do well in 2015:

Byung-ho Park Born 19860710 Age 28 Bats R Throws R Height 73 Weight 235 No DT, Real

Year Team         Lge  AB  H   DB  TP  HR  BB  SO  R  RBI  SB  CS  Out  BA   OBP  SLG  EqA EqR POW SPD KRt WRt BIP 
2006 LG_Twins____ KBO 130  21   2   0   5   9  42   7  13   1   3  113 .162 .227 .292 .185   8  14  -2 -21  -4 -25 
2009 LG_Twins____ KBO 188  41   7   0   9  20  70  28  25   2   1  149 .218 .304 .399 .227  18  19  -2 -32  -2  -5 
2010 LG_Twins____ KBO 160  30   4   0   7  26  55  25  22   5   1  135 .188 .305 .344 .228  17  15   1 -23   6 -21 
2011 Nexen/LG     KBO 201  51  11   2  13  26  76  31  31   2   0  151 .254 .345 .522 .292  35  38  -2 -28   2   7 
2012 Nexen_Heroes KBO 469 136  34   0  31  73 111  76 105  20   9  349 .290 .392 .561 .325 105  30   2  -5   6   5 
2013 Nexen_Heroes KBO 450 143  17   0  37  92  96  91 117  10   2  315 .318 .437 .602 .337 104  34  -2   1  11   4 
2014 Nexen_Heroes KBO 455 139  16   2  52  96 140 126 124   8   3  323 .305 .436 .692 .328  99  61   0 -17  12  -1 
2015 Nexen_Heroes KBO 528 181  35   1  53  78 161 129 146  10   3  354 .343 .436 .714 .336 116  52   0 -15   5  21 
 --------------------------------------------------------------------------------------------------------
 Minors               546 156  27   1  43  88 162 107 121  12   4 2040 .285 .391 .577 .308 105  39   0 -15   6   4 

Two straight years with over 50 HR, albeit with a LOT of strikouts. Remember, this is untranslated, real stats.

The Orioles' guy, Kim, is a very different hitter:
Hyun-soo Kim           Born 19880112   Age 27     Bats L Throws R       Height 74 Weight 220       No DT, Real
Year Team         Lge  AB  H  DB  TP  HR   BB  SO   R  RBI  SB  CS  Out  BA   OBP  SLG  EqA EqR POW SPD KRt WRt BIP 
2006 Doosan_Bears KBO   1  0    0   0   0   0   0   0   0   0   0    1 .000 .000 .000 .000   0   0   0   0   0  -1 
2007 Doosan_Bears KBO 319  87  19   3   5  26  46  33  32   5   2  237 .273 .335 .398 .263  42   1  -2   3  -3   2 
2008 Doosan_Bears KBO 470 168  34   5   9  80  50  83  89  13   8  312 .357 .454 .509 .332  99   3  -1  14   8  20 
2009 Doosan_Bears KBO 482 172  31   6  23  80  59  97 104   6   6  322 .357 .447 .589 .322  95  11  -2  13   6  16 
2010 Doosan_Bears KBO 473 150  29   0  24  78  64  88  89   4   8  339 .317 .414 .531 .306  88  16  -3  11   7   4 
2011 Doosan_Bears KBO 475 143  25   2  13  71  63  71  91   5   3  344 .301 .392 .444 .291  79   6  -3  11   5   4 
2012 Doosan_Bears KBO 437 127  17   1   7  46  50  47  65   6   3  318 .291 .358 .382 .271  61   1  -3  11   0   3 
2013 Doosan_Bears KBO 434 131  23   1  16  62  71  63  90   2   4  319 .302 .382 .470 .286  70  12  -4   6   4   3 
2014 Doosan_Bears KBO 459 147  26   0  17  53  45  75  88   2   0  317 .320 .396 .488 .280  66   8  -4  18   1  -2 
2015 Doosan_Bears KBO 512 167  26   0  28 101  63 103 121  11   5  359 .326 .438 .541 .312  98  13  -2  18  11   0 
 --------------------------------------------------------------------------------------------------------
 Minors               554 176  31   2  19  81  68  90 105   7   5 2868 .318 .406 .488 .298  95   8  -3  12   5   6 

Kim is a high-contact hitter, not a power guy.

Based on their last three years in Korea, then:
1) Kang hit for a .323 eqa, and would be expected to hit about .283 in the US from the slope above
2) Park hit .333 in 2013-15, which slopes to .293;
3) Kim hit .294, which goes to .257

When making a full set of translations, I look at players who have played in both the questioned league and leagues 
I already know about. By far and away the largest set of year to year matches for Korea are players who went from US
AAA leagues to Korea. On the aggregate, the translation works very well:

            AB     H    DB  TP   HR   BB    SO  SB  CS    BA  OBA  SLG  EQA  EQR   
 Yr1 (3A)  6216. 1443. 319. 27. 202. 658. 1248. 91. 30. .232 .306 .390 .247  726. 
 Yr2 (KR)  6235. 1483. 323. 30. 209. 639. 1204. 89. 41. .238 .309 .400 .251  746. 

The translations end up looking like this:

Jeong-ho Kang            Born 19870405 Age 28 Bats R Throws R Height 72 Weight 180 Regular DT 
Year Team         Lge  AB  H   DB  TP  HR  BB  SO  R  RBI  SB  CS  Out   BA  OBP  SLG  EqA  EqR POW SPD KRt WRt BIP
2012 Nexen_Heroes KBO 449 126  28   1  24  55  74  73  73  17   4  332 .281 .361 .508 .294  78  17   3   4   4  -3 
2013 Nexen_Heroes KBO 469 114  21   1  18  63  99  60  75  11   7  369 .243 .333 .407 .259  63   9  -1  -3   5  -8
2014 Nexen_Heroes KBO 430 121  33   2  24  48  99  74  74   2   3  314 .281 .361 .535 .297  75  26  -3  -7   2   5 
2015 Pittsburgh__ NL  417 128  25   2  16  31  79  69  66   5   5  295 .307 .378 .492 .294  69  10  -1   0  -3  14
 --------------------------------------------------------------------------------------------------------
 Majors               417 128  25   2  16  31  79  69  66   5   5  295 .307 .378 .492 .294  69  10  -1   0  -3  14
 Minors               568 152  35   2  28  70 115  87  94  13   6 1015 .268 .351 .481 .283  91  17   0  -2   4  -2 
 Total                571 158  35   2  27  64 114  89  93  11   6 1310 .277 .357 .484 .286  92  15   0  -2   2   1

Expected .283, got .294. His BABIP (which is notoriously flaky) was rather high in Pittsburgh, and made up for somewhat disappointing power performance.

Byung-ho Park Born 19860710 Age 28 Bats R Throws R Height 73 Weight 235 Regular DT 
Year Team         Lge  AB  H   DB  TP  HR  BB  SO  R  RBI  SB  CS  Out  BA   OBP  SLG  EqA EqR POW SPD KRt WRt BIP 
2013 Nexen_Heroes KBO 467 125  15   1  29  54  88  73  89   8   2  350 .268 .345 .490 .284  75  20  -2   0   3  -7 
2014 Nexen_Heroes KBO 471 114  16   2  32  53 130  86  78   6   3  364 .242 .325 .488 .274  72  29   0 -15   2 -10 
2015 Nexen_Heroes KBO 544 151  32   2  33  58 138  96  99   8   3  400 .278 .351 .526 .293  93  26   0 -11   1   5 
 --------------------------------------------------------------------------------------------------------
 Minors               574 151  24   2  36  64 138  99 103   9   3 1114 .263 .341 .503 .284  93  25   0  -9   2  -4

Comparables: Mauro_Gomez Rudy_York Bucky_Jacobsen Richie_Sexson


Hyun-soo Kim Born 19880112 Age 27 Bats L Throws R Height 74 Weight 220 Regular DT 
Year Team         Lge  AB  H   DB  TP  HR  BB  SO  R  RBI  SB  CS  Out   BA  OBP  SLG  EqA EqR POW SPD KRt WRt BIP 
2013 Doosan_Bears KBO 449 112  23   1  14  55  64  58  72   1   3  352 .249 .325 .399 .253  57   6  -5   8   4 -10 
2014 Doosan_Bears KBO 472 120  17   1  13  50  42  58  63   2   0  357 .254 .328 .377 .249  55   1  -5  17   1 -12 
2015 Doosan_Bears KBO 528 140  21   1  19  70  54  77  89   9   5  403 .265 .350 .417 .268  75   4  -2  15   5 -10 
 --------------------------------------------------------------------------------------------------------
 Minors               567 146  24   1  18  69  63  76  88   5   3 1112 .257 .335 .398 .258  73   4  -4  13   3 -11

Comps: Pete_O’Brien, Frederick_Hopke, Clay_Dalrymple, Jim_Delsing

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