I've been playing WIS Basketball for a few years. I know that when searching for players, you use the 'advanced stats' like reb% and ast% instead of rbs/game & ast/g.. My question is, in WIS MLB, what advanced stats should I be looking for? For example: Should I search for OAV# or OAV+? Which is more relative to the sim engine?

A separate question- I have messed around looking at the player database. I was searching with the '#' numbers. like OVA#, WHIP#, etc. I was assuming that the guys with the lowest WHIP#s combined with the lowest OAV#s would have the best ERAs in the sim. I looked at the 'past performances' on several players.. It seems like guys like Tiant and Koufax perform worse than with similar WHIP#s but worse OAV#s. Guys with OAV#s over .200 and WHIP#s .81-1 are actually performing better than guys with OAV#s at .190 or lower with similar WHIP#s.. Why is this (other than HR/9)- I have taken that into account, but, in my mind, that doesn't seems to make up for the difference I see when I look at past performances. Tiant and Koufax both have past performance ERAs over 3 and a lot guys with worse OAV#s and similar WHIPSs have ERAs between 2.5 and 3. I don't understand how to figure out which guys translate the best to the SIM, bc when I was doing my original search, Tiant & Koufax looked like they would be Gems based on OAV# and WHIP#.. Thanks for any help/advice

Milest, the + stats compare the player to that season's average, so they are very relevant to a single season league such as certain theme leagues that use players from one season only or progressive leagues that use single season stats. 

The # stats compare the player to the overall baseball history average 1885-2015, so these are more relevant to Open Leagues. The + ratings won't help you that much in those leagues, because your pitcher from 1975 or 1966 is not facing batters from that same era but rather from every era. So you need to know how they rate compared to overall history 

An example: since 1930 was a mega-hitters' season like 1999 or 1894, the best pitchers from that season will be very good compared to other pitchers from the same season, but the batters from 1930 will also hit well against them and so perhaps their OAV  is a good one all things considered, and if they were really good, might have a an OAV +140 or WHIP+ 140. But when up against batters from other seasons, if their best is still only average compared with the baseball history average, then they will not do well. On the other hand, if they bucked a trend, such as 1999 Pedro in a mega hitters' year, they may be world-beating compared with the overall baseball average, since their official stats understate  how good they were compared with the time in which they played. 

Grossly oversimplifying, the deadball era pitchers that d_rock97 refers to have numbers that when compared with the overall history of baseball (the # stats) are still very good, hardly any home runs for example, so they are very tough in OLs or leagues with players from all eras. 

So, in general - single season league, go with +, multi-season and Open leagues, give priority to # stats.

Neither WHIP nor OAV takes into account extra base hits.  They are useful, but my preferred stat for an initial search is ERC#.  ERC stands for Component ERA, and it's not a stat that you will find in too many places, but it takes into account the pitcher's propensity for allowing extra base hits.

Here are the top 24 pitchers in the WIS database, with IP/162 ot 250-450, ranked by ERC#.

Notice that many of the deadball era guys have higher OAV# than the modern guys, but comparable or better ERC#.  This is largely because they tended to allow fewer extra base hits.

Player	Team	IP	WHIP#	OAV#	HR/	ERC#	SALARY
		/162			9#
Johnson, Walter	1913 Washington Senators	364	0.81	.195	0.40	1.47	$16,372,767
Maddux, Greg	1994 Atlanta Braves	288	0.89	.204	0.14	1.51	$13,307,875
Walsh, Ed	1910 Chicago White Sox	392	0.90	.198	0.22	1.58	$15,524,600
Mathewson, Christy	1909 New York Giants	292	0.89	.210	0.12	1.59	$11,872,286
Joss, Addie	1908 Cleveland Naps	342	0.90	.210	0.10	1.61	$12,778,253
Alexander, Pete	1915 Philadelphia Phillies	402	0.91	.198	0.12	1.62	$16,221,577
Johnson, Walter	1912 Washington Senators	394	0.92	.196	0.09	1.67	$17,525,356
Koufax, Sandy	1965 Los Angeles Dodgers	336	0.90	.186	0.62	1.67	$14,039,062
Gibson, Bob	1968 St. Louis Cardinals	305	0.92	.194	0.36	1.68	$12,216,556
Koufax, Sandy	1963 Los Angeles Dodgers	311	0.93	.197	0.48	1.71	$12,224,296
Brown, Mordecai	1908 Chicago Cubs	329	0.94	.211	0.05	1.72	$12,741,189
Hendrix, Claude	1914 Chicago Whales	381	0.96	.196	0.23	1.72	$14,589,740
Guidry, Ron	1978 New York Yankees	272	0.95	.194	0.40	1.73	$10,616,274
Scott, Mike	1986 Houston Astros	276	0.94	.191	0.50	1.73	$11,044,208
Mathewson, Christy	1908 New York Giants	411	0.93	.216	0.21	1.74	$14,894,040
Brown, Mordecai	1909 Chicago Cubs	363	0.94	.212	0.05	1.74	$14,434,556
Sutton, Don	1972 Los Angeles Dodgers	285	0.95	.196	0.41	1.75	$10,970,609
Tiant, Luis	1968 Cleveland Indians	260	0.95	.184	0.55	1.77	$9,944,786
Ford, Russ	1910 New York Highlanders	322	0.96	.198	0.22	1.79	$12,111,223
Adams, Babe	1919 Pittsburgh Pirates	307	0.96	.223	0.06	1.80	$11,925,673
Mathewson, Christy	1905 New York Giants	359	0.98	.209	0.19	1.81	$13,568,855
Cicotte, Eddie	1917 Chicago White Sox	365	0.97	.213	0.10	1.81	$12,970,244
Chance, Dean	1964 Los Angeles Angels	279	1.04	.203	0.18	1.83	$11,057,607
Bernhard, Bill	1902 Cleveland Blues	259	0.94	.215	0.25	1.83	$8,914,136

I included the salary column because it makes it immediately obvious why 1908 Joss is the most commonly used starting pitcher in open leagues.

68 Tiant and 65 Koufax have very low OAV, but allow many more HR than just about any other pitcher on the list.  This is (likely) the reason they do not do as well as you might think.

Now here's the same pitchers, comparing their ERC# with their performance history ERA:

Player	Team	IP	ERC#	PH
		/162		ERA
Johnson, Walter	1913 Washington Senators	364	1.47	2.67
Maddux, Greg	1994 Atlanta Braves	288	1.51	2.56
Walsh, Ed	1910 Chicago White Sox	392	1.58	2.73
Mathewson, Christy	1909 New York Giants	292	1.59	2.88
Joss, Addie	1908 Cleveland Naps	342	1.61	2.72
Alexander, Pete	1915 Philadelphia Phillies	402	1.62	2.79
Johnson, Walter	1912 Washington Senators	394	1.67	2.68
Koufax, Sandy	1965 Los Angeles Dodgers	336	1.67	3.10
Gibson, Bob	1968 St. Louis Cardinals	305	1.68	2.94
Koufax, Sandy	1963 Los Angeles Dodgers	311	1.71	3.09
Brown, Mordecai	1908 Chicago Cubs	329	1.72	2.94
Hendrix, Claude	1914 Chicago Whales	381	1.72	2.97
Guidry, Ron	1978 New York Yankees	272	1.73	3.25
Scott, Mike	1986 Houston Astros	276	1.73	3.25
Mathewson, Christy	1908 New York Giants	411	1.74	2.98
Brown, Mordecai	1909 Chicago Cubs	363	1.74	2.97
Sutton, Don	1972 Los Angeles Dodgers	285	1.75	3.08
Tiant, Luis	1968 Cleveland Indians	260	1.77	3.11
Ford, Russ	1910 New York Highlanders	322	1.79	3.03
Adams, Babe	1919 Pittsburgh Pirates	307	1.80	3.01
Mathewson, Christy	1905 New York Giants	359	1.81	3.16
Cicotte, Eddie	1917 Chicago White Sox	365	1.81	3.11
Chance, Dean	1964 Los Angeles Angels	279	1.83	3.43
Bernhard, Bill	1902 Cleveland Blues	259	1.83	3.18

There's a pretty strong correlation there.  Dean Chance has very few seasons in his PH, so I'm going to eliminate him.  Using the other 23 pitchers, I get a correlation of 0.81, which is extremely strong.

Keep in mind that performance history ONLY included performance from open leagues.

For those whose eyes glaze over at advanced statistics, feel free to ignore the following...it's interesting, but not that relevant to understanding the SIM.  And for those who are experts in advanced stats, yes I know that I am oversimplifying and ignoring a lot of stuff.  

I used Excel to fit a linear model predicting ERA from ERC#, and it gives: ERA = (1.63*ERC#)+ 0.2

The p-value on the intercept is not statistically significant, so for all practical purposes I'm going to consider it zero.  ERC# predicts ERA very well for this group of pitchers, with an R² of 0.66.  

If I fit a similar model using WHIP# as the independent variable, I get ERA = (3.65*WHIP#) - 0.4.  Again the intercept is not significant.  The R² now is only 0.47, not nearly as strong.

OAV# by itself is not a good predictor of ERA.  And using OAV# and WHIP# together does not significantly improve the model over using WHIP# by itself.  

Sweet!! Thanks for the information!

Don't forget ballpark Milest. You pitchers will perform better in ballparks that yield fewer runs. Build your lineup to fit your ballpark. If you go for low OAV, low HR allowed, and low walks allowed you should be fine.