Valentine’s Day has come and gone, which means that the players and teams
are all getting stretched out and warmed up to get 2014 spring training under
way. For many of us fans and fantasy
geeks, this is where we can start to see how things will shape up. For those of us who are completely confident
going into the draft and for those who have an idea but are afraid of making a
bad pick or reach, I wanted to provide a simple list of top players in each
position. This list is very useful for
5x5 formats because I am focusing on the top players in each position based on
where they fall in the ranks of each offensive stat (for hitters: R, RBI, HR,
SB, AVG) and pitching stats (for pitchers: ERA, W, K, SV, WHIP) when compared to the other players in their respective position. We all know the top players in each position
and how valuable they can be to a team, but what happens once you get deeper
into rounds, the talent starts to plateau, and you can’t tell whether to
pick a certain player over another. You
can look and see who has more strikeouts, but he may not get many wins, or you
may have an option of two mid-low tier shortstops and pick the one that gets
more steals even though he may be lacking in the other stats required to
succeed in 5x5 scoring. What I have done
isn’t new, and it’s pretty basic, but for the average fantasy player or
sabermetric noob, this list can come in handy for those late round picks. Every pick counts.
For each of the ten stats, for
example, RBI, we find the mean for what each player is projected for in 2014
based off of rotochamp.coms player rankings in each position. The average
number of projected RBI’s by the top 55 catchers is 40.818, using software, I
also find the standard deviation which gives us the spread throughout this
sample of catchers. The main goal for
each position in each stat is to find the z-score of each player. We find the z-score of a player by taking the
number of projected RBI (or any other of the nine stats) for that player, and
subtract that number by the sample mean of RBI for catchers. We will then divide that number by our method
of spread, which is the standard deviation, 20.797. This number output, is the z-score for a player in RBI. This number, the z-score, is the
number of standard deviations away from the sample mean. By seeing how far away or close this player
is to the position average of projected RBI, we are one step closer to being able to
comparing players based on projected performance in all five stats. Now this is just one z-score for one player
in one stat, to really take advantage of these numbers and understand how a
player performs in all required stats, this needs to be done for all players and
stats.
With a list of the top 55-100 (sample size of players varies based on position) players’ z-scores in each stat, we can see which
players are projected to give you the best overall numbers in all five stats
combined for each position of hitters and pitchers.
Each player will have five different z-scores, one for each stat. So for
example, a players’ z-score in one or two categories may be in the top tier,
while maybe his other z-scores in the remaining categories falls in the middle
or lower tier. Whatever the case may be, this makes it hard to compare players
of the same position when they excel in different categories. To get one number that portrays each players
value in the five categories combined, we add up these five z-scores for each player. Once these five z-scores are added, we now we have a new number and this number is the sum of the five z-scores, or the 5x5 number, for the player. This
number gives us an idea of where the player ranks overall with the rest of the
players in the same position. The player
with the highest 5x5 number in a certain position will most likely provide the best
value in all five stat categories, whereas a player with a lower score may have
high value in one or two categories and lower value in the other categories. The players in each position are ranked by the
new score we got by adding up the players five z-scores, now we have a list of players in each
position ranked by how much more or less better than average they are in all five
stats combined, and how they compare, overall, to other players in their
position. Like I’ve said, this list is
very helpful in determining a player draft pick in late rounds. When you want players that will do the most
for your team in all categories, you now have a list of numbers that tells you
where each player ranks and compares with the rest of the players in his position in each
stat.
This
list and tactic is strictly a quantitative based player ranking and does not
take into account match-ups, teams, opponents, schedules, or any other of the
many variables in play. That’s why there
are other formulas and sabermetric stats which focus on these different aspects
of the game.
To get started, I will show you a table of what I have done for the top 55 catchers based on 2014 projected stats from rotochamp.com . It's a standard table format showing each player ranked by their 5x5 numbers. You can see the z-score for each player and stat as well. Surprisingly, Lucroy takes the top spot in this stat, and looking at his z-scores, he is quality in the first three categories, and quite above average in SB and AVG. Many of us would go with Posey or Santana over Lucroy, so remember this table is more of a tool used to decide players in later rounds. It still does give us a good look at where each player compares with the rest of his position mates and can be useful in 5x5 fantasy drafts.
| Catchers | Runs | HR | RBI | SB | AVG | Total(5x5) |
| Lucroy | 1.074 | 1.05 | 1.78 | 3.82 | 2.31 | 10.034 |
| Rosario | 1.515 | 2 | 1.26 | 1.49 | 1.505 | 7.77 |
| Molina | 1.019 | 0.478 | 1.403 | 2.07 | 2.74 | 7.71 |
| Santana | 2.176 | 1.529 | 2.028 | 0.91 | 0.591 | 7.234 |
| Posey | 1.73 | 1.05 | 1.98 | 0.328 | 1.88 | 6.968 |
| McCann | 1.34 | 2.166 | 1.69 | -0.253 | 0.967 | 5.91 |
| Mauer | 2.28 | 0.095 | 1.499 | 0.328 | 1.559 | 5.761 |
| Gattis | 1.349 | 2.96 | 2.26 | -0.836 | -0.107 | 5.626 |
| Weiters | 1.239 | 1.75 | 1.45 | 0.328 | 0.591 | 5.358 |
| Martin | 0.964 | 0.89 | 0.393 | 3.82 | -0.86 | 5.207 |
| Perez | 1.129 | 0.89 | 1.45 | -0.836 | 1.77 | 4.403 |
| Saltalamaccia | 1.408 | 1.05 | 1.06 | 0.91 | -0.215 | 4.213 |
| Ramos | 0.468 | 1.75 | 0.73 | -0.253 | 1.451 | 4.146 |
| Castro | 1.074 | 0.89 | 0.922 | 0.328 | 0.591 | 3.805 |
| Navarro | 0.137 | 1.21 | 0.585 | -0.253 | 1.236 | 2.915 |
| Ruiz | 0.247 | -0.542 | 0.105 | 0.328 | 1.72 | 1.858 |
| Pierzynski | 0.192 | 0.478 | 0.633 | -0.253 | 0.752 | 1.802 |
| Jaso | 0.854 | -0.542 | -0.039 | 1.49 | -0.537 | 1.226 |
| Soto | 0.468 | 0.732 | 0.249 | 0.328 | -0.86 | 0.917 |
| Gomes | 0.523 | 0.478 | 0.105 | -0.253 | -0.269 | 0.584 |
| Avila | 0.468 | 0.254 | 0.393 | -0.253 | -0.322 | 0.54 |
| Castillo | 0.192 | -0.223 | 0.153 | 0.328 | -0.161 | 0.289 |
| M.Montero | 0.743 | 0.095 | 0.585 | -0.836 | -0.322 | 0.265 |
| Zunino | 0.523 | 0.254 | -0.183 | -0.253 | -0.645 | -0.304 |
| Meseraco | -0.193 | -0.063 | 0.008 | -0.836 | 0.752 | -0.332 |
| Ellis | 0.027 | -0.223 | 0.057 | -0.836 | -0.053 | -1.028 |
| Grandal | 0.302 | -0.382 | -0.327 | -0.836 | 0 | -1.243 |
| Suzuki | -0.35 | -0.86 | -0.472 | 0.382 | -0.053 | -1.353 |
| Ianetta | 0.247 | 0.095 | -0.183 | -0.253 | -1.344 | -1.438 |
| Arencebia | -0.469 | 0.245 | -0.376 | -0.836 | -0.236 | -1.672 |
| Doumit | -0.579 | -0.542 | -0.376 | -0.836 | 0.483 | -1.85 |
| Hundley | -0.524 | -0.382 | -0.52 | -0.253 | -0.322 | -2.001 |
| Pinto | -0.542 | 0.095 | -0.568 | -0.836 | -0.215 | -2.066 |
| Flowers | -0.358 | 0.095 | -0.424 | -0.253 | -1.451 | -2.391 |
| Pena | -1.02 | -1.18 | -0.76 | -0.253 | 0.752 | -2.461 |
| Conger | -0.909 | -0.542 | -0.616 | -0.253 | -0.215 | -2.535 |
| Phegley | -1.02 | -0.86 | -0.712 | 0.382 | -0.537 | -2.747 |
| Kottaras | -0.744 | -0.223 | -0.808 | -0.253 | -0.806 | -2.834 |
| Hanigan | -0.469 | -1.18 | -0.568 | -0.836 | -0.107 | -3.16 |
| Cervelli | -1.13 | -0.86 | -1.193 | -0.253 | 0.215 | -3.221 |
| Norris | -0.579 | -0.542 | -0.76 | -0.91 | -0.484 | -3.275 |
| J.Molina | -0.524 | -1.019 | -0.664 | 0.328 | -1.505 | -3.384 |
| J.Montero | -1.185 | -0.86 | -1.145 | -0.253 | -0.053 | -3.496 |
| Ross | -0.909 | -0.542 | -0.808 | -0.253 | -1.129 | -3.641 |
| Lobaton | -0.579 | -0.86 | -0.856 | -0.253 | -1.129 | -3.677 |
| Vogt | -1.02 | -1.019 | -1.145 | -0.253 | -0.376 | -3.813 |
| d'Arnaud | -1.075 | -1.019 | -0.279 | -0.836 | -0.645 | -3.854 |
| Buck | -1.075 | -0.701 | -1.145 | -0.253 | -0.806 | -3.98 |
| Hernandez | -1.35 | -1.019 | -1.241 | -0.253 | -0.215 | -4.078 |
| Thole | -1.13 | -1.49 | -1 | -0.836 | 0.107 | -4.349 |
| McKenry | -1.075 | -0.701 | -0.904 | -0.836 | -1.075 | -4.591 |
| Recker | -1.185 | -0.86 | -1.289 | -0.253 | -1.129 | -4.716 |
| Blanco | -1.075 | -1.019 | -1.049 | -0.836 | -1.129 | -5.108 |
| Sanchez | -1.295 | -1.18 | -1.93 | -0.836 | 0.053 | -5.188 |
| Corporan | -1.35 | -0.86 | -1.93 | -0.836 | -0.752 | -5.728 |
I will post and discuss a little bit about each position shortly and have each done, hopefully before spring training games start. And from there we can start getting a better look at how these numbers will represent themselves when put into action on the field.
*Here is the rotochamp.com link where I obtained the projected stats used: http://www.rotochamp.com/baseball/PlayerRankings.aspx?Position=Catcher
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