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

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

 

• Batting
• Pitching
• Fielding
• Minors
• Projections

  

JOSE FERNANDEZ

Born 7-31-1992

Bats R Throws R

Actual Pitching Statistics

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

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	SV	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	0	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	0	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	0	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	0	8.0	.7	2.5	9.9
				541.7	440	183	41	155	13	554	3.04	41	21	0	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	—	—	–

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.

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

Card(s): minors, majors

 

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.

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.

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.

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

 

 

Those who have known me for years may remember that I have my own Hall of Fame method set up, which works something like this.

Each player gets a score that is a modeled on the MVP ballot. It is a weighted average of his career WARP3 – your best season in WARP counts 14 times (multiply WARP for that season by 14), then your second best season counts 9, third best 8, fourth best 7, and so on down to tenth – your tenth best, and all future seasons beyond tenth, count for 1. It achieves a sort of balance – decide for yourselves how properly weighted – between peak seasons and career length. The highest score in history belongs to Babe Ruth, with a 745. The lowest score that go someone into my Hall was 294 (Ned Garver, in 1968). We can safely say it takes a score of 300 just to make the conversation, and a 400 to have real shot. However, the score alone is no guarantee, because of the eligibility rules.

Starting from 1936, the year the actual Hall began, I begin adding players to the my Hall. The best players available, according to the MVP carer score, get the slots. I do limit my choices to players who have been retired for between 5 and 20 years (same as the real Hall). I allow one Hall of Fame opening per 12 real team-seasons in major league history – this was approximately the number of real Hall slots when I first developed the system about 10 years ago. There were some complicated rules regarding how to catch up from 1936-60, but that is all out of the way now. With 30 major league teams playing today, it means that my Hall is going to grow by 5 players every two years, alternating between 2 and 3 player classes.

2016, as it happens, is a three player class, and I do give a place to three players on the genuine ballot – Ken Griffey, Mike Mussina, and Trevor Hoffman.

Griffey is on the ballot for the first time, has a 458 MVP score, which ranks sixth among center fielders, and is the third-highest score on the ballot. He’s in easily.

Mike Mussina has a 434 MVP score and 100 career WARP3; his MVP score is 17th among starting pitchers. He was the first runner-up on my 2014 ballot, did that again in 2015, and makes it in 2016.

Trevor Hoffman has a 429 MVP score, which makes him the #3 Relief pitcher – behind Mariano Rivera and Joe Nathan, and just ahead of Goose Gossage.

My runners-up this year start with Sammy Sosa (425; he will get over the top in 2018), followed by Gary Sheffield (425, makes it in 2020), then Jim Edmonds (414), Jeff Kent (403), and Nomar Garciaparra (398). Edmonds is the first runner-up in 2020, and looks to have a good chance for 2021 induction.

The problem I would have in filling out a real ballot is that there are 11 players on it who are already in my own Hall, plus two more who will make it by 2020, plus another couple of possibilities beyond that.

In 2002, I put Alan Trammell (455) in on the first ballot. I did the same for Mark McGwire (432) in 2007, and for Tim Raines (457) in 2008, and for Edgar Martinez (461) in 2010, and for Jeff Bagwell (395) in 2011.

In 2013, with two slots available, I inducted Barry Bonds (744) and Roger Clemens (600) – the second and fifth highest MVP-career scores of all time.

And then in 2014 I put in Mike Piazza (446), plus the three from this year.

If you pressed me for who to drop, I’d pick between Bagwell (whose 395 is the lowest score) and Hoffman (I think my relief pitcher ratings are skewed for recent years, so I have less confidence in the values).

Scores for players on the ballot not already mentioned: Garret Anderson (207), Brad Ausmus (217), Luis Castillo (164), David Eckstein (184), Troy Glaus (352, which makes him #20 among 3B), Mark Grudzielanek (167), Mike Hampton (255), Jason Kendall (321), Mike Lowell (243), Fred McGriff (310), Curt Schilling (385 – was 3rd runner-up in 2013, and fifth runner-up in 2014), Lee Smith (339), Mike Sweeney (171), Billy Wagner (374), Larry Walker (311), and Randy Winn (254).

 

My full Hall list

johnswa01 WALTER JOHNSON 1936 SP 666.2
wagneho01 HONUS WAGNER 1936 SS 560.2
alexape01 PETE ALEXANDER 1936 SP 548.2
cobbty01 TY COBB 1936 CF 545.5
lajoina01 NAP LAJOIE 1936 SB 511.6
Runners-up 1936 mathech01 collied01 speaktr01 walshed01 barnero01

mathech01 CHRISTY MATHEWSON 1937 SP 505.7
collied01 EDDIE COLLINS 1937 SB 497.8
speaktr01 TRIS SPEAKER 1937 CF 488.4
walshed01 ED WALSH 1937 SP 479.2
barnero01 ROSS BARNES 1937 SB 474.5
Runners-up 1937 broutda01 connoro01 delahed01 ewingbu01 whitede01

broutda01 DAN BROUTHERS 1938 FB 471.1
connoro01 ROGER CONNOR 1938 FB 462.7
delahed01 ED DELAHANTY 1938 LF 462.5
ewingbu01 BUCK EWING 1938 C 442.7
whitede01 DEACON WHITE 1938 C 441.4
Runners-up 1938 youngcy01 ansonca01 kellyki01 flickel01 mcgrajo01

youngcy01 CY YOUNG 1939 SP 438.8
ansonca01 CAP ANSON 1939 FB 428.8
kellyki01 KING KELLY 1939 RF 418.8
flickel01 ELMER FLICK 1939 RF 404.6
mcgrajo01 JOHN MCGRAW 1939 TB 401.0
Runners-up 1939 glassja01 bennech01 hamilbi01 dahlebi01 jennihu01

glassja01 JACK GLASSCOCK 1940 SS 397.9
bennech01 CHARLIE BENNETT 1940 C 397.5
hamilbi01 BILLY HAMILTON 1940 CF 387.4
dahlebi01 BILL DAHLEN 1940 SS 386.8
jennihu01 HUGHIE JENNINGS 1940 SS 378.9
Runners-up 1940 jacksjo01 willine01 planked01 colliji01 dunlafr01

ruthba01 BABE RUTH 1941 RF 744.7
vanceda01 DAZZY VANCE 1941 SP 374.1
jacksjo01 JOE JACKSON 1941 RF 369.0
willine01 NED WILLIAMSON 1941 TB 368.7
Runners-up 1941 planked01 colliji01 dunlafr01 thompsa01 childcu01

planked01 EDDIE PLANK 1942 SP 365.8
colliji01 JIMMY COLLINS 1942 TB 347.5
dunlafr01 FRED DUNLAP 1942 SB 347.3
thompsa01 SAM THOMPSON 1942 RF 346.4
Runners-up 1942 childcu01 cicoted01 seweljo01 heilmha01 wadderu01

hornsro01 ROGERS HORNSBY 1943 SB 537.0
cochrmi01 MICKEY COCHRANE 1943 C 391.2
friscfr01 FRANKIE FRISCH 1943 SB 370.1
childcu01 CUPID CHILDS 1943 SB 344.7
Runners-up 1943 cicoted01 seweljo01 heilmha01 wadderu01 ruckena01

cicoted01 EDDIE CICOTTE 1944 SP 344.5
seweljo01 JOE SEWELL 1944 SS 344.1
heilmha01 HARRY HEILMANN 1944 RF 343.0
wadderu01 RUBE WADDELL 1944 SP 342.9
Runners-up 1944 ruckena01 davisge01 hinespa01 chancfr01 brownth01

gehrilo01 LOU GEHRIG 1945 FB 513.6
ruckena01 NAP RUCKER 1945 SP 339.6
davisge01 GEORGE DAVIS 1945 SS 337.4
hinespa01 PAUL HINES 1945 CF 337.4
Runners-up 1945 chancfr01 brownth01 richaha01 wallabo01 covelst01

chancfr01 FRANK CHANCE 1946 FB 336.9
brownth01 THREE_FINGER BROWN 1946 SP 332.1
richaha01 HARDY RICHARDSON 1946 SB 331.7
wallabo01 BOBBY WALLACE 1946 SS 330.8
Runners-up 1946 covelst01 keelewi01 tiernmi01 rusieam01 veachbo01

hartnga01 GABBY HARTNETT 1947 C 414.3
grovele01 LEFTY GROVE 1947 SP 385.1
ferrewe01 WES FERRELL 1947 SP 334.8
covelst01 STAN COVELESKI 1947 SP 329.8
Runners-up 1947 keelewi01 tiernmi01 rusieam01 veachbo01 longhe01

gehrich01 CHARLIE GEHRINGER 1948 SB 410.9
keelewi01 WILLIE KEELER 1948 RF 329.1
tiernmi01 MIKE TIERNAN 1948 RF 327.9
rusieam01 AMOS RUSIE 1948 SP 327.7
Runners-up 1948 veachbo01 longhe01 sislege01 clarkjo01 bancrda01

hubbeca01 CARL HUBBELL 1949 SP 422.6
veachbo01 BOBBY VEACH 1949 LF 324.0
longhe01 HERMAN LONG 1949 SS 323.7
sislege01 GEORGE SISLER 1949 FB 321.6
Runners-up 1949 clarkjo01 bancrda01 terrybi01 sheckji01 whitnji01

simmoal01 AL SIMMONS 1950 LF 347.8
clarkjo01 JOHN CLARKSON 1950 SP 321.4
bancrda01 DAVE BANCROFT 1950 SS 317.1
terrybi01 BILL TERRY 1950 FB 315.7
Runners-up 1950 sheckji01 whitnji01 burkeje01 grohhe01 wheatza01

foxxji01 JIMMIE FOXX 1951 FB 438.0
cronijo01 JOE CRONIN 1951 SS 418.7
wanerpa01 PAUL WANER 1951 RF 417.4
johnsbo01 BOB JOHNSON 1951 LF 320.0
Runners-up 1951 warnelo01 camildo01 sheckji01 whitnji01 burkeje01

dickebi01 BILL DICKEY 1952 C 442.7
lyonste01 TED LYONS 1952 SP 320.2
warnelo01 LON WARNEKE 1952 SP 319.0
bartedi01 DICK BARTELL 1952 SS 318.7
Runners-up 1952 camildo01 sheckji01 whitnji01 burkeje01 grohhe01

ottme01 MEL OTT 1953 RF 513.8
hermabi01 BILLY HERMAN 1953 SB 374.1
greenha01 HANK GREENBERG 1953 FB 353.5
deandi01 DIZZY DEAN 1953 SP 345.3
Runners-up 1953 passecl01 hackst01 camildo01 sheckji01 whitnji01

vaughar01 ARKY VAUGHAN 1954 SS 446.9
medwijo01 JOE MEDWICK 1954 LF 392.0
passecl01 CLAUDE PASSEAU 1954 SP 330.9
hackst01 STAN HACK 1954 TB 319.4
Runners-up 1954 camildo01 sheckji01 whitnji01 burkeje01 grohhe01

camildo01 DOLPH CAMILLI 1955 FB 317.3
sheckji01 JIMMY SHECKARD 1955 LF 315.1
whitnji01 JIM WHITNEY 1955 SP 313.3
burkeje01 JESSE BURKETT 1955 LF 313.2
Runners-up 1955 grohhe01 cliftha01 wheatza01 galanau01 orourji01

gordojo01 JOE GORDON 1956 SB 380.0
applilu01 LUKE APPLING 1956 SS 372.1
waltebu01 BUCKY WALTERS 1956 SP 356.3
Runners-up 1956 grohhe01 cliftha01 wheatza01 galanau01 orourji01

dimagjo01 JOE DIMAGGIO 1957 CF 474.5
doerrbo01 BOBBY DOERR 1957 SB 382.3
grohhe01 HEINIE GROH 1957 TB 312.1
Runners-up 1957 cliftha01 wheatza01 galanau01 orourji01 clarkfr01

boudrlo01 LOU BOUDREAU 1958 SS 413.9
cliftha01 HARLOND CLIFT 1958 TB 311.9
wheatza01 ZACK WHEAT 1958 LF 310.4
Runners-up 1958 galanau01 orourji01 clarkfr01 faberre01 gosligo01

mizejo01 JOHNNY MIZE 1959 FB 407.3
leonadu02 DUTCH LEONARD 1959 SP 325.0
nichobi01 BILL NICHOLSON 1959 RF 324.5
Runners-up 1959 galanau01 orourji01 clarkfr01 faberre01 gosligo01

galanau01 AUGIE GALAN 1960 LF 310.1
orourji01 JIM O’ROURKE 1960 LF 309.4
clarkfr01 FRED CLARKE 1960 LF 309.3
Runners-up 1960 faberre01 gosligo01 ruffire01 jackstr01 seymocy01

newhoha01 HAL NEWHOUSER 1961 SP 423.7
kinerra01 RALPH KINER 1961 LF 354.0
stephve01 VERN STEPHENS 1961 SS 313.4
Runners-up 1961 ruffire01 kellech01 elliobo01 kleinch01 stanked01

fellebo01 BOB FELLER 1962 SP 448.2
robinja02 JACKIE ROBINSON 1962 SB 419.9
ruffire01 RED RUFFING 1962 SP 307.8
Runners-up 1962 kellech01 elliobo01 kleinch01 stanked01 rosenal01

camparo01 ROY CAMPANELLA 1963 C 379.5
troutdi01 DIZZY TROUT 1963 SP 335.5
kellech01 CHARLIE KELLER 1963 LF 306.8
Runners-up 1963 elliobo01 kleinch01 stanked01 rosenal01 peskyjo01

reesepe01 PEE_WEE REESE 1964 SS 327.9
Runners-up 1964 lemonbo01 elliobo01 kleinch01 stanked01 rosenal01

lemonbo01 BOB LEMON 1965 SP 307.7
elliobo01 BOB ELLIOTT 1965 TB 304.8
Runners-up 1965 kleinch01 stanked01 slaugen01 rosenal01 peskyjo01

willite01 TED WILLIAMS 1966 LF 587.6
stanked01 EDDIE STANKY 1966 SB 301.4
Runners-up 1966 slaugen01 rosenal01 peskyjo01 freylo01 brechha01

slaugen01 ENOS SLAUGHTER 1967 RF 295.7
Runners-up 1967 garvene01 rosenal01 peskyjo01 freylo01 brechha01

ashburi01 RICHIE ASHBURN 1968 CF 438.4
garvene01 NED GARVER 1968 SP 293.5
Runners-up 1968 rosenal01 peskyjo01 freylo01 brechha01 sainjo01

musiast01 STAN MUSIAL 1969 LF 572.0
wynnea01 EARLY WYNN 1969 SP 313.8
Runners-up 1969 hodgegi01 schoere01 rosenal01 peskyjo01 freylo01

snidedu01 DUKE SNIDER 1970 CF 318.1
piercbi02 BILLY PIERCE 1970 SP 308.9
Runners-up 1970 hodgegi01 schoere01 rosenal01 peskyjo01 brechha01

spahnwa01 WARREN SPAHN 1971 SP 470.2
berrayo01 YOGI BERRA 1971 C 397.8
Runners-up 1971 hodgegi01 foxne01 schoere01 rosenal01 peskyjo01

roberro01 ROBIN ROBERTS 1972 SP 420.5
koufasa01 SANDY KOUFAX 1972 SP 404.5
Runners-up 1972 frienbo01 hodgegi01 foxne01 schoere01 rosenal01

frienbo01 BOB FRIEND 1973 SP 307.9
hodgegi01 GIL HODGES 1973 FB 304.1
Runners-up 1973 foxne01 schoere01 rosenal01 peskyjo01 fordwh01

mantlmi01 MICKEY MANTLE 1974 CF 490.9
matheed01 EDDIE MATHEWS 1974 TB 457.0
Runners-up 1974 foxne01 schoere01 rosenal01 peskyjo01 fordwh01

drysddo01 DON DRYSDALE 1975 SP 383.1
boyerke01 KEN BOYER 1975 TB 370.8
Runners-up 1975 foxne01 schoere01 rosenal01 peskyjo01 fordwh01

foxne01 NELLIE FOX 1976 SB 300.7
schoere01 RED SCHOENDIENST 1976 SB 299.6
Runners-up 1976 rosenal01 fordwh01 colavro01 sainjo01 jacksla01

bankser01 ERNIE BANKS 1977 SS 414.6
bunniji01 JIM BUNNING 1977 SP 370.7
Runners-up 1977 pascuca02 rosenal01 fordwh01 colavro01 jacksla01

clemero01 ROBERTO CLEMENTE 1978 RF 363.2
wilheho01 HOYT WILHELM 1978 RP 310.5
Runners-up 1978 pascuca02 mazerbi01 fordwh01 colavro01 jacksla01

mayswi01 WILLIE MAYS 1979 CF 637.8
pascuca02 CAMILO PASCUAL 1979 SP 310.3
mazerbi01 BILL MAZEROSKI 1979 SB 298.5
Runners-up 1979 fordwh01 callijo01 colavro01 jacksla01 antonjo02

santoro01 RON SANTO 1980 TB 459.8
kalinal01 AL KALINE 1980 RF 377.8
Runners-up 1980 cepedor01 cashno01 fordwh01 callijo01 colavro01

gibsobo01 BOB GIBSON 1981 SP 467.3
maricju01 JUAN MARICHAL 1981 SP 389.8
Runners-up 1981 cepedor01 cashno01 killeha01 fordwh01 callijo01

aaronha01 HANK AARON 1982 RF 550.0
robinfr02 FRANK ROBINSON 1982 RF 477.7
Runners-up 1982 willibi01 cepedor01 freehbi01 cashno01 killeha01

wynnji01 JIM WYNN 1983 CF 421.5
allendi01 DICK ALLEN 1983 TB 410.7
Runners-up 1983 torrejo01 robinbr01 willibi01 cepedor01 freehbi01

torrejo01 JOE TORRE 1984 C 358.0
woodwi01 WILBUR WOOD 1984 SP 345.5
Runners-up 1984 robinbr01 willibi01 cepedor01 freehbi01 cashno01

munsoth01 THURMAN MUNSON 1985 C 350.7
robinbr01 BROOKS ROBINSON 1985 TB 344.9
willibi01 BILLY WILLIAMS 1985 LF 325.9
Runners-up 1985 cepedor01 whitero01 freehbi01 cashno01 hunteca01

mccovwi01 WILLIE MCCOVEY 1986 FB 373.7
cepedor01 ORLANDO CEPEDA 1986 FB 325.3
Runners-up 1986 whitero01 freehbi01 cashno01 hunteca01 hillejo01

bondsbo01 BOBBY BONDS 1987 RF 344.9
whitero01 ROY WHITE 1987 LF 319.4
Runners-up 1987 marshmi01 freehbi01 cashno01 hunteca01 hillejo01

tiantlu01 LUIS TIANT 1988 SP 345.9
stargwi01 WILLIE STARGELL 1988 LF 325.3
Runners-up 1988 smithre06 marshmi01 freehbi01 cashno01 hunteca01

benchjo01 JOHNNY BENCH 1989 C 505.3
yastrca01 CARL YASTRZEMSKI 1989 LF 416.6
Runners-up 1989 perryga01 jenkife01 tenacge01 campabe01 smithre06

morgajo02 JOE MORGAN 1990 SB 508.5
perryga01 GAYLORD PERRY 1990 SP 415.3
Runners-up 1990 palmeji01 jenkife01 tenacge01 campabe01 smithre06

carewro01 ROD CAREW 1991 SB 409.1
palmeji01 JIM PALMER 1991 SP 390.1
jenkife01 FERGIE JENKINS 1991 SP 382.9
Runners-up 1991 fingero01 tenacge01 campabe01 smithre06 marshmi01

grichbo01 BOBBY GRICH 1992 SB 473.2
seaveto01 TOM SEAVER 1992 SP 468.6
Runners-up 1992 rosepe01 fingero01 tenacge01 campabe01 fostege01

rosepe01 PETE ROSE 1993 LF 405.5
jacksre01 REGGIE JACKSON 1993 RF 395.6
Runners-up 1993 niekrph01 fingero01 tenacge01 campabe01 fostege01

carltst01 STEVE CARLTON 1994 SP 462.1
niekrph01 PHIL NIEKRO 1994 SP 375.3
Runners-up 1994 fingero01 tenacge01 conceda01 nettlgr01 cruzjo01

schmimi01 MIKE SCHMIDT 1995 TB 552.5
fingero01 ROLLIE FINGERS 1995 RP 368.9
evansda01 DARRELL EVANS 1995 TB 367.3
Runners-up 1995 tenacge01 conceda01 nettlgr01 cruzjo01 bellbu01

hernake01 KEITH HERNANDEZ 1996 FB 383.9
tenacge01 GENE TENACE 1996 C 367.2
Runners-up 1996 conceda01 nettlgr01 cruzjo01 lemonch01 bellbu01

evansdw01 DWIGHT EVANS 1997 RF 418.2
conceda01 DAVE CONCEPCION 1997 SS 359.5
Runners-up 1997 nettlgr01 cruzjo01 lemonch01 bellbu01 campabe01

cartega01 GARY CARTER 1998 C 474.6
blylebe01 BERT BLYLEVEN 1998 SP 422.2
nettlgr01 GRAIG NETTLES 1998 TB 358.1
Runners-up 1998 guerrpe01 cruzjo01 randowi01 lemonch01 bellbu01

brettge01 GEORGE BRETT 1999 TB 480.9
yountro01 ROBIN YOUNT 1999 SS 429.6
Runners-up 1999 ryanno01 fiskca01 tananfr01 guerrpe01 cruzjo01

ryanno01 NOLAN RYAN 2000 SP 403.9
fiskca01 CARLTON FISK 2000 C 403.7
gossari01 RICH GOSSAGE 2000 RP 389.5
Runners-up 2000 tananfr01 guerrpe01 cruzjo01 randowi01 lemonch01

whitalo01 LOU WHITAKER 2001 SB 373.6
mattido01 DON MATTINGLY 2001 FB 368.5
Runners-up 2001 tananfr01 puckeki01 guerrpe01 cruzjo01 randowi01

trammal01 ALAN TRAMMELL 2002 SS 454.8
smithoz01 OZZIE SMITH 2002 SS 415.2
dawsoan01 ANDRE DAWSON 2002 CF 383.1
Runners-up 2002 violafr01 tananfr01 puckeki01 guerrpe01 cruzjo01

sandbry01 RYNE SANDBERG 2003 SB 428.7
violafr01 FRANK VIOLA 2003 SP 373.1
Runners-up 2003 murraed02 tananfr01 puckeki01 guerrpe01 cruzjo01

molitpa01 PAUL MOLITOR 2004 TB 422.5
eckerde01 DENNIS ECKERSLEY 2004 SP 396.2
murraed02 EDDIE MURRAY 2004 FB 370.2
Runners-up 2004 tananfr01 puckeki01 guerrpe01 cruzjo01 randowi01

boggswa01 WADE BOGGS 2005 TB 493.2
phillto02 TONY PHILLIPS 2005 LF 392.4
Runners-up 2005 tananfr01 puckeki01 guerrpe01 cruzjo01 randowi01

belleal01 ALBERT BELLE 2006 LF 455.0
hershor01 OREL HERSHISER 2006 SP 383.2
clarkwi02 WILL CLARK 2006 FB 372.5
Runners-up 2006 tananfr01 goodedw01 puckeki01 jonesdo01 guerrpe01

ripkeca01 CAL RIPKEN 2007 SS 559.0
mcgwima01 MARK MCGWIRE 2007 FB 432.2
Runners-up 2007 saberbr01 gwynnto01 fernato01 tananfr01 goodedw01

raineti01 TIM RAINES 2008 LF 457.3
saberbr01 BRET SABERHAGEN 2008 SP 403.6
gwynnto01 TONY GWYNN 2008 RF 389.9
Runners-up 2008 finlech01 fernato01 tananfr01 goodedw01 puckeki01

henderi01 RICKEY HENDERSON 2009 LF 594.3
finlech01 CHUCK FINLEY 2009 SP 379.3
Runners-up 2009 coneda01 fernato01 tananfr01 goodedw01 puckeki01

martied01 EDGAR MARTINEZ 2010 OT 460.6
alomaro01 ROBERTO ALOMAR 2010 SB 437.6
larkiba01 BARRY LARKIN 2010 SS 435.8
Runners-up 2010 venturo01 coneda01 fernato01 appieke01 tananfr01

brownke01 KEVIN BROWN 2011 SP 409.9
bagweje01 JEFF BAGWELL 2011 FB 395.2
Runners-up 2011 venturo01 olerujo01 coneda01 fernato01 appieke01

willibe02 BERNIE WILLIAMS 2012 CF 406.4
venturo01 ROBIN VENTURA 2012 TB 382.1
olerujo01 JOHN OLERUD 2012 FB 379.6
Runners-up 2012 coneda01 fernato01 appieke01 tananfr01 palmera01

bondsba01 BARRY BONDS 2013 LF 744.2
clemero02 ROGER CLEMENS 2013 SP 600.2
Runners-up 2013 piazzmi01 sosasa01 schilcu01 coneda01 fernato01

maddugr01 GREG MADDUX 2014 SP 534.2
thomafr04 FRANK THOMAS 2014 FB 470.2
piazzmi01 MIKE PIAZZA 2014 C 445.7
Runners-up 2014 mussimi01 sosasa01 kentje01 gonzalu01 schilcu01

johnsra05 RANDY JOHNSON 2015 SP 494.9
martipe02 PEDRO MARTINEZ 2015 SP 483.5
Runners-up 2015 mussimi01 sosasa01 sheffga01 kentje01 garcino01

griffke02 KEN GRIFFEY 2016 CF 457.7
mussimi01 MIKE MUSSINA 2016 SP 433.9
hoffmtr01 TREVOR HOFFMAN 2016 RP 428.6
Runners-up 2016 sosasa01 sheffga01 edmonji01 kentje01 garcino01

rodriiv01 IVAN RODRIGUEZ 2017 C 483.7
ramirma02 MANNY RAMIREZ 2017 LF 433.2
Runners-up 2017 posadjo01 sosasa01 sheffga01 edmonji01 kentje01

rolensc01 SCOTT ROLEN 2018 TB 428.6
posadjo01 JORGE POSADA 2018 C 428.5
sosasa01 SAMMY SOSA 2018 RF 425.3
Runners-up 2018 sheffga01 santajo01 edmonji01 jonesch06 kentje01

riverma01 MARIANO RIVERA 2019 RP 594.9
hallaro01 ROY HALLADAY 2019 SP 451.8
Runners-up 2019 sheffga01 santajo01 edmonji01 heltoto01 jonesch06

giambja01 JASON GIAMBI 2020 FB 443.8
sheffga01 GARY SHEFFIELD 2020 RF 424.6
santajo01 JOHAN SANTANA 2020 SP 415.1
Runners-up 2020 edmonji01 heltoto01 jonesch06 kentje01 thomeji01

2015 was a bad year for stats.

I had intended to write this as a review of my 2015 projections – and I will get to that – but I ran into something else while working them and decided that this was actually a bigger deal.

I’ve got a program here which it seems I haven’t tun in several years, judging from the last modified datestamp, which calculates the accuracy of a bunch of run estimators. Here’s the chart for my own stat, Equivalent Runs, going back to 1955:

This is the root-mean-square error for the difference between actual runs scored and Equivalent Runs, for all major league teams, by year. EqR’s average error for the entire 1955-2016 time period was 20.54 runs; the error for 2016, by contrast, was 26.22, 28% worse than normal. It was EqR’s second-worst performance in these 60 years; only 1990, with a 26.59 rmse, was worse.

One of the dirty little secrets of run estimators is that they tend to correlate better with each other than they do with actual runs scored, as they are all using variations of themes to do their estimating. And so EqR’s problem was everybody’s problem. WOBA’s estimates were 26% than their 60-year average in 2016, and also had its worst season since 1990. BaseRuns was 21% worse, worst since 1985. My own version of BaseRuns was 26% worse than average, and had its absolute worst season of the period. Total Average was 17% worse than normal, Runs Created 12% worse than normal, Palmer’s Linear Weights 16% worse, OPS 11% worse. Anyway you slice it, the relationship between statistics and runs hit a little bit of a rough patch in 2015.

But the trouble doesn’t end there. The other key tool in our sabermetric box is to convert runs scored and allowed into wins and losses. Bill James’ Pythagorean formula still stands up rather well – the only real innovation is to let the exponent vary according to runs per game; I use the Pythagenpat idea, using RPG^0.285.

Like run estimators, the win estimator’s accuracy varies from year to year:

The 60-year average error here is 3.92 wins, but in 2016 it came in 4.65 – sixth worst over the time period. It has the appearance of a downward trend, which is almost entirely attributable to 1955 being such a bad outlier. The key contributors in 2016 were Toronto and Oakland, both of whom were more than 9 wins short of their Pythaorean expectations; meanwhile a full half-dozen teams beat their win expectation by more than 5 runs.

We can combine these two into a composite estimator. First, we need to get them onto a common scale. Normally, I would say runs, divided by 10, would get you in the neighborhood , but I am going to divide by 5 instead. I have two reasons for that:

1. The ratio of the average errors, 20.54 for EqR and 3.92 for Pythagorean wins, is close to 5;
2. I am using the error in runs estimation as a proxy to also stand in for the error in runs allowed estimation, which properly should be its own independent factor. For what it is worth, in 2015, the runs allowed estimates were even worse than the runs scored estimates – an RMSE of 26.8. So I am essentially doubling up on the runs and runs allowed, and then dividing by 10, which is a net of 5.

Here is that chart, then:

The combined score here is 9.89 wins, far worse than the 9.25 in 2005, which had ranked as the worst score since 1955.

Here’s the observation that got me looking into this. When I compared my pre-season projections from April 1 to reality, I found this:

 

–                               Correlation     RMSE

Actual wins               .334              10.11

1st-order wins            .431               9.20

2^nd-order wins           .537               9.06

 

My statistical scores with reality were pretty terrible, IMO. I found it very interesting, though how much better the statistics were when I looked at first-order wins (i.e., Pythagorean wins – expected wins based on actual runs scored and allowed) and then even more so with second-order wins (calculating Pythagorean wins from expected runs scored and allowed instead of actual runs scored and allowed). I would expect that this may be commonplace, albeit less extreme – the breakdown in our stat/result relationship made those differences larger than usual.

So, in those respects, good riddance to 2015.