Since I refer to the Stat Score rather frequently when it comes to wide receivers, I thought I should lay out how it all works. Unfortunately, this is something that starts off very simple, and proceeds to get weirder and more questionable as I try to untangle the mess of numbers that I use. To keep things as clear as possible, I'll try to explain it all in steps, before I get to some of the more deranged issues.
On its most basic level, it revolves
around one core idea. How much of a team's offense was a receiver
responsible for? Well, that is simple to figure out. If a college team
gained 5000 yards, and a receiver in that offense gained 1000 yards,
then he was involved in/responsible for 20% of the total yards. So, why
should we care about this percentage? The reason for this is that raw
yardage doesn't tell us very much about how valuable a player was to
their team. For instance, look at this example:
Team's Total Offense Receiver's Yardage % of Offense
6000 1200 20%
5000 1000 20%
4000 900 22.5%
you can see, as the team's total output goes up, the receiver has to
produce even more to be as proportionally valuable. While the receiver
who generated only 900 yards initially appears less impressive, he was possibly more valuable to his team's offense than the player with 1200
yards. It's easy to get excited about a draft prospect who has gaudy
stats, but sometimes we lose track of the context in which the stats are
generated. A player who is generating a larger percentage of his
team's offense is probably going to be the focus of the opposing
defense. So, generating significant stats, while being the main focus
of your team is more challenging.
As I've mentioned before, this is very similar to Shawn Siegele's Dominator Rating
system. The only real difference is that I base the percentage off of
the total offense, and Shawn does it based off of the passing offense. In the end, it is sort of like the difference between Dr. Pepper versus Mr. Pibb. It's debatable which approach is better, and maybe I am wrong to do it
this way, but I am too lazy to change my database now. I think that a
team's ability to run the ball should be a part of the score, as it
alleviates some of the pressure on the receivers, so I take that into
Beyond this difference of opinion, relating to
the running game, there is one other issue. Instead of giving a score
for the player's last year in college, I use their last 2 years. If one
year of production is good, two is better. This is just my way of
weeding out one year wonders. The average percentage of the offense
that a draft prospect is responsible for in his last college year is 17.75%. In their next to last year, the average is 15.34%.
The actual averages would really be lower, if I considered all
receivers, and not just the ones who had historically gotten drafted.
I have these scores I convert them, to see how many standard.deviations
the result is away from the average, because being able to make bell
curves is neat, if perhaps somewhat pointless. You could really just
stick with the basic percentage and still be perfectly fine, but I like
to complicate things for myself. One standard deviation for their final
year would be 6.581%, and for their next to last year 6.674%.
So, the math works like this for their final year (next to last year is
the same, but obviously putting in a different set of numbers, and the
correct corresponding standard deviation):
(Player's % of offense as senior - 17.75)/6.581
resulting score lets you calculate what percentile the player would be
in, or create a bell curve to see how common their results are.
Generally, though distributions can vary from position to position, or
subject to subject, one positive standard deviation will place a player
near the 70th percentile, and one negative deviation around the 30th
Now things start to get weird. Just for
the sake of being able to easily peruse a player's numbers I will
combine some of their scores to create a sort of overall score. It's
generally a bad idea to rely too much on these overall scores,
especially when they start to combine large numbers of smaller scores,
as they can hide weird imbalances or irregularities. It's kind of like
judging someone based on their college GPA, instead of looking at their grades
in individual classes. The grades in individual classes will give you a
better picture of what is going on. Maybe someone was average in 6 out
of eight classes, excelled in one, and bombed another. If that one
class he bombed was something important (Nuclear Physics for Dummies), and the one he excelled in was
something stupid (Basket Weaving 101), that is worth making a note of.
Combining scores into one larger score, just lets me get a quick glimpse
of a player, though it can be less accurate sometimes. I'll still refer to them
in my posts for the sake of simplicity, but I'll also try to point out
areas of concern when they arise.
issue/observation that I should mention. It seems to me that smaller
players tend to perform at a higher level in college, at a younger age,
while the bigger guys tend to really hit their stride around their
junior or senior year. If you see a player with multiple 1000 yard
seasons, he is more often going to be built to a more average size.
This is just mad speculation on my part, but I have to wonder if this
has something to do with the bigger guys needing more time to grow into
their frame, or reach their physical peak, compared to a 5'10" and 190#
player, who might have finished growing by his senior year of high
school. Like I said, mad speculation. Either way, I combine their 2
years of production in different ways because of this. For smaller guys
(under 210#), I value their final 2 years evenly, in a 50/50 split. For the
bigger guys (over 200#) I value their 2 years with an emphasis on their
final year, with a 70/30 split. This is all a highly debatable and
possibly stupid thing to do, but for now, that's what I'm doing.
last thing I do is to look at their average yards per catch over both
years (though I just average their two years here, and don't worry about the two separate averages), and their number of
receptions in both years. This is also highly questionable, but I like
having the data. I'll list here the average result for the 2 years and
the standard deviation. Calculating how many standard deviations away
from the average a result is can be done the same as I mentioned
Yards Per Catch Average Standard Deviation
Combined 2 year Avg. 14.966 2.645
Avg. # of Receptions Standard Deviation
Final Year 62.06 23.6
Next to Last Year 51.75 22.68
just like to have these scores to get an idea of how big an impact a
player has on a per play basis, and what their total volume of plays
looks like. To some extent, a player who does well in YPC will tend to
have a lower volume of receptions, and vice versa. So, a player is
unlikely to score well in both. Extremely explosive deep threats
generally have fewer receptions, and reliable high volume underneath
guys tend to have a lower YPC. It's just something I like to look for,
but it isn't terribly important, so I usually only let it count for
about 10-20% of a player's total Stat Score, depending on their size.
Since the two scores tend to knock each other out, they usually have
very little overall impact. This is just something I sprinkle in there as a garnish.
In the end, I wouldn't get
too carried away with trying to divine mystical meanings from these
scores. A lot of stat geeks are trying to find the 'One Stat To Rule
Them All', and I'm not convinced that such a thing exists. I'll use
stats that sometimes might appear to be trying to reach this goal, but I
don't take them too seriously. I'm just trying to find an acceptable standard for making comparisons between prospects. I'm not trying to make any claims of having found the one true answer. The stats are useful for drawing your eye
towards the exceptional versus the mundane, but beyond that you are
still going to be better informed when you look at the broader set of data that goes into these stats.. This is an ESPN world though, and people seem to want
overly simple answers to relatively complex questions. With that said,
even a method such as this, that is at the very least questionable, one
can get a better sense of a player than just raw stats from college.
As time moves on, I will undoubtedly make alterations to my views on certain stats, and will update past numbers, and explain when/why I'm making such changes. Or, at some point, my eyes will fall out from staring at spreadsheets for too long.