Log in Register

Login to your account

Username *
Password *
Remember Me

Create an account

Fields marked with an asterisk (*) are required.
Name *
Username *
Password *
Verify password *
Email *
Verify email *

fb mb tw mb

Wednesday 22nd Feb 2017

We are at the time of the season where it is all about points.   You reserve a more valuable player for a less valuable player if activating the less valuable player can net you more rotisserie points.   Since the National Fantasy Baseball Championship is a no-trade contest, all the manager really has at their disposal is liberal use of their reserve list and the free agent wire.   But the shrewd manager also has history on their side, which if exploited properly, can aid in amassing the maximum number of points.

The conventional advice in an essay such as this is to examine your categories in an effort to decide where you can gain the most points while losing the fewest.  It is pointed out, as if you did not know, that every league is different.  Each league has its own statistical distribution within each category.  Where you sit within each category dictated your mode of action, where to attack and where to back off.

And of course, all of this is true and sound advice, but it really is not anything every owner who is still paying attention does not already do.  So instead, we will focus on a concept that may be new, but uses history as an ally to help you manage your squad down the stretch.

What we are going to do is take a look at normalized end of the season standings.  While the numbers change a bit year to year, the general distribution within average NFBC standings if consistent.  Average NFBC standings are generated by averaging the first place through the last place total in each category.  This is how you would generate category drafting targets.  Most like to target draft by aiming for the second or third place total in each category.  We are going to use these totals in a different manner.

Normalizing the average standings converts the real categorical totals so that the sum of all 15 teams adds up to the same number, we will use 1000.  In other words, instead of there being fewer total homers than say RBI, and instead of batting average, ERA and WHIP being expressed as a decimal, the sum total of all 15 team’s normalized totals will be the same, in this instance 1000.  This allows us to do determine the relative distribution of stats within each category.  Doing it this way helps debunk a myth or two about category management.  Of course, we are going to look at average standings.  As suggested, what is paramount is where you lie within your particular league.

For the ease of formatting, only the first place and last place normalized totals will be presented.  Difference is between first and last place while regression is a least squares linear regression from 2nd place to 14th place, to eliminate the outliers from owners punting or overloading categories.   The smaller the difference and regression are, the more tightly bunched the category sits, suggesting it may be more possible to gain ground in those specific areas.

 

BA

R

HR

RBI

SB

ERA

W

WHIP

K

SV

1st

70.7

73.5

79.8

74.6

91.6

76.4

80.9

71.7

74.8

102.1

15th

64.4

57.4

54.5

57.5

43.3

58.0

50.5

62.2

52.0

20.9

difference

6.3

16.1

25.3

17.1

48.3

18.4

30.4

9.5

22.8

81.1

regression

0.46

1.00

1.47

0.94

4.73

0.99

1.63

0.53

1.37

4.73

Let us start at the categories with the largest distribution, steals and saves.  There are a couple of things to keep in mind here.  First, of all the stats, these are the two that are most frequently strategically punted by some owners, rendering the last place total artificially low.  In addition, even though the spread is quite large, both these categories are dominated by players that earn their respective stat in bunches, that is a smaller percentage of the player pool contributes a huge proportion of the stats, making the big gaps easier to overcome.

Now let us jump to the other end of the spectrum and take a gander at the categories that are most tightly grouped.  Lo and behold, it is the ratio categories, especially batting average.  Truth be told, this is the real crux of this discussion, as this suggests to me that it may be easier to gain points in batting average, ERA and WHIP than many perceive, due to the tightly grouped distribution.  Granted, as the innings and at bats mount, the ratios do not move very much, but what also is true is they do not need to move very much in order to pass a competitor.  In addition, unless you owned George Brett, circa 1983, once you get a counting stat, it is yours to keep.  You do not lose stats.  But your ratios can in fact get worse.  So not only are the ratio categories tightly bunched, but you can gain rotisserie points without any of your hurlers throwing a single pitch if a competitor ahead of you has a pitcher get lit up.  If you glean anything from this discussion, the take-home lesson is not to automatically assume you are locked into place in batting average, ERA and WHIP.  The truth is globally, more points will be gained (hence lost) in the ratio categories.  How you look to take advantage rests with how you stand in the counting categories.  Perhaps deploying a solid middle reliever can help you net a couple more ratio points if you are situated at a point where you cannot gain or lose points in wins and strikeouts.  The point is, too many brush off the possibility due to feeling they have too many innings accrued to have a middle reliever make a difference.   This is a myth; he still can contribute in a positive manner to ERA and WHIP.

Looking at the pitching counting stats, the common advice is not to chase wins as they are unpredictable and out of the player’s control.  And that is true.  But additionally, based on difference and regression data, it is a wee bit more difficult to make up ground as the spread between teams is broader.  Of course, this can be overcome by deploying more starters, but that brings you back to the unpredictability of wins and how chasing them may cost you more in ratios, for even if you are fortunate to pad your win total, on a relative basis you may need more of them to propel yourself up in the standings.  Strikeouts is probably the most reliable pitching stat to project, but again based on the difference and regression data, you will really need to ratchet up your total, hopefully not at the expense of your ratios, which as just explained, are not as stable as you may have thought.

It is not too surprising that runs and RBI show similar profiles as they are both team dependent stats, reliant as much on how many at bats your team totals as it does the quality of your hitting attack.  Next to the ratios, runs and RBI points are the easiest to earn based on difference and regression data.  What this means is it is imperative that you maximize your at bats down the stretch, making liberal use of the Friday activation rule and continually scouring the free agent pool for hitters with the most playing time.  Something else to keep in mind is movement in the counting stats is facilitated by some dormant squads no longer interested in managing their team to optimal efficiency.  When “doing the math” and adding up potential points you can gain, it behooves the attentive owner to go the extra yard and look for teams whose totals will suffer down the stretch as they have some roster holes.  This makes catching them that much easier.

So while the overriding factor is still where you are nestled in each category, there are some global nuances you can employ to help you gain the most points.  At the forefront is not ignoring ratios, followed by maximizing at bats.  Then the trick is balancing the pitching ratios versus wins and strikeouts and batting power versus speed.

Todd Zola won the 2008 and 2009 NFBC $1300 Auction Las Vegas National League only championships.  After a one-year hiatus, he intends to return in 2011 to reclaim his title.

 

Add comment


Security code
Refresh

Latest Tweets

 2017Platlogo

 

LABRLOGO

xfl

toutwarslogo-new

Our Authors