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Stats!
Stats! Stats! For almost 15 years I crunched numbers relentlessly for
several Wall Street firms, trying to uncover the magic formula that would
make someone riches. Although I was not the one gaining the riches, it
provided me with a perspective that was never conveyed in school. History
repeats itself; whether it’s relating to the prepayment habits of an
interest rate sensitive consumer, the behavioral aspects of the stock
market, or baseball statistics.
Remember when all those guys on CNN
Financial told us that the internet economy was a new economy for the
market and the “old fundamentals” were now useless …baloney, history
repeats itself! Here’s
the difficult concept to understand: Why does history repeat itself? If
you can answer this question, regardless of what you’re analyzing,
you’ll be one step ahead of everyone else. Here’s the key word:
Limitations. Are there limitations to the economic growth
cycles? We will leave that thought for another website.
Back
to Baseball: Why does history repeat itself when accessing a baseball
player’s ability? Yep you guessed it...Limitations! There are
limitations in a ball player’s ability, they come in two forms: physical
and mental. Rey Ordonez is never going to hit 50 homeruns in a MLB season,
even if he IV’s a direct line of horse growth hormones for 3 years.
It’s just not within his physical structure. Rey has a certain range of
offensive abilities that are quantifiable and forecast-able within a
certain degree of accuracy. On the other end of this spectrum is a player
like Craig Biggio who for years was able to synergy his mental &
physical attributes enabling himself among the fantasy elite.
Unfortunately, players like Biggio are also the type for fade off fast, as
their body’s age. While a player like Jose Canseco can still mash a ball
500 feet at 36 with a wretched back, a player like Biggio needs 100% of
youthful health.
We
know, again within a certain degree of accuracy, that batters do not hit a
peak until their 28th birthday, some sooner, some later, but
within a bell shaped range we know what to reasonably expect given the
limitations of each player.
Coming
out of the minors we know that there will be a discrepancy in a player’s
first year stats, verses his minor league stats. However, based on his
past performances we can reasonably access who has and doesn’t have the
potential to post a decent WHIP or strikeout total. It’s not exact, but
it’s reasonable and it relies on history repeating itself.
As
important as it is to follow history in an up trending pattern during a
player’s career and conversely down trending during their latter
seasons, it’s also important to throw out the anomalous seasons. This is
just part of what our projections program does.
We
have a collection of baseball data since about 1880 contained in our
databases, with just about every major league stat relating to player’s
individual season. Using a hybrid non-linear regression formula, we
backward integrated the data and basically asked our statistics program (MatLab)
to find the most important variables that have any statistical relevance
from year to year. Using the end result of variables (moving trend
analysis being the most weighed) we are able to produce results that are
within acceptable ranges in predicting a player’s forward season.
From
that point we scrutinize each players computed projection and make what I
call “subjective injections”. Factors
such as playing time are always subjective, as are human elements, such as
emotions, which obviously can not be factored by a machine, thus we
intercede with the final result on at least 25% of the players (100% based
on playing time expectations).
Last
season was a bit of an anomaly, although we able to spot many comeback
seasons: 18 of 25 to be exact. In
hindsight our overall projection numbers were a bit conservative on the
batters. In fact, this new trend is rising at an alarming rate: Hitters,
either because of the smaller ballparks, juiced balls, a reduced
strikezone, steroids, or the “diminished quality among pitchers”, are
producing staggering stats when taken as
a group. This may change in
2001, since an enforced “high strikezone” will surely aid many
pitchers.
As
a final note, reliable forecasting would be extremely difficult if it were
not for the large number of games played during a season. A full season of
162 games provides us with a whole lot of information to make a reliable
forecast based on the supple sample size.
Our
Winning Draft Strategy: VAM
We’re
beginning to see our theory of VAM (Value Above Mean) catch up
throughout the fantasy community this year. Position scarcity is a
theory that outdates my experience as a Sports Statistician, however we
were the first to put it in a formula, quantify it, and post it as a
draft day advantage.
Value
Above Mean (or Average) is a
computation that measures a players' fantasy worth versus others at
their respective position/s. In other words, what we do is take the
average fantasy values for the typical number of fantasy starters at each
position. This average serves as the standard that we compare all others
at that particular position by. We additionally take it a step further and
sometimes assign probabilities to the less reliable positions.
VAM
is dependent on the number of players that will be drafted in your league.
Many make the assumption that sites that post rankings according to
positional scarcity, are going to work for their league…think again!
On-line we base that number on a typical league with 12 teams (2 leagues),
14 batters (2 C, 3 Corner, 3 MI, 5 OF, 1 UT) and 9 pitchers (6 starters, 3
Relief). However with our
Excel software, you can target the real “positional scarcity”
according to your league. Try it out, change the number of teams or
players, and you’ll see a different ranking in most of the scenarios.
Why
does VAM work? VAM works
because of the limited amount of Dollars (auction draft) we have to spend, or the limited
amount of superstar talent we draft (straight draft). It’s an optimization formula: “How
do I get the most bang for my dollar or draft pick?”. By paying a
little bit more for those position players, such as catchers, that will
outperform others at their respective position, by greater margins than
can be found at other positions. In simple terms why pay $30 for Ken
Griffey (OF) when you can purchase Mike Piazza for $30. Their fantasy
stats will be comparable, but Piazza will outperform the typical catcher
by 60%, while Griffey will only outperform the typical outfielder by 15%.
Thus you capture that incremental gain of 45%…doing this over and over
again you can see how the odds stack up in you favor. Thus even if the
projections are off, and they will be, the odds from your draft
strategy (VAM) will certainly aid you in the overall makeup of your team.
After
the top 90 or so players (see
chart) the advantage of this drafting method begins to rapidly
fade off, since you’re now getting in the in the mid range players
who do not offer any substantial gain over the typical player at these
positions.
The
disruptors to this concept are of-course injuries; you can be the lucky
guy who loses Pedro Martinez, Alex Rodriguez, and Mike Piazza. The other
disruptor is the reliability of your projections for different groups of
players. As just about everyone knows, Pitchers are a statistician’s
nightmare, since many have such inconsistent results from year to year.
Thus we have to value them cautiously before they get to the VAM stage, or
give them less probability during the VAM calculations.
Good
Luck this Fantasy Season! |
