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Enough About WAR. Let’s Get RAW!

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Enough About WAR. Let’s Get RAW!

Posted on 14 February 2012 by Daniel Aubain

Let’s be honest. Most of us baseball fans have learned more about WAR over the last two years of baseball’s Hall of Fame voting than we ever did in years of taking American History classes from middle school through college. Okay, okay. Different kinds of war. I get it. One is a heated battle between those who feel they are right and will go to any lengths to prove it while fighting to the death against those who disagree and the other is a state of armed conflict between different nations or states or different groups within a nation or state.

Alright, enough with making the yuck yuck. I consider myself an avid learner and as a life-long fan of baseball on the field and someone who is well into his second decade of playing fantasy baseball, gaining a basic understanding of the more popular sabermetric statistics and concepts seems like the logical progression of my obsession. And no topic is more hotly discussed and debated than the idea of WAR (Wins Above Replacement). So debated that there are actually two versions of WAR: FanGraphs‘ fWAR and Baseball-Reference‘s rWAR.

Now, I’m not here to share my elementary understanding of all that goes into calculating WAR or the pros and cons of using it to compare which player is more valuable to a team than another or who is more Hall of Fame-worthy. I’m here to mix up the letters of WAR and present to you my version of a fake sabermetric statistic know as RAW. And what would RAW be without two version of itself: dRAW and RAWs. Nothing helps a fake statistic catch on more than the ease of saying it as a real world word. WHIP isn’t just a cream to fantasy baseball fans.

There are only two pieces of data you need to know in order to calculate dRAW and RAWs: Runs and Walks. Catchy fake statistics should also be easy to calculate if you want people to remember them and think they possibly could become real ones with just a little bit more effort put in by the creator.

So now that we have the data we need, Runs and Walks, what are the formulas used to calculate the values of these new, fake statistics? Simple. Firstly, dRAW is calculated by dividing a player’s Runs by Walks. Or, to be more accurate to its name, divide Runs Above Walks. Secondly, RAWs is calculated by adding a player’s Runs and Walks. Or, to be more accurate to its name, Runs And Walks sum.

So let’s review some of the points up until now that could make a fake statistic go mainstream. The name of the statistic(s) should be easy to say. CHECK! It should be easy to calculate. Easy division and easy addition. CHECK! And I guess the data should be readily available. CHECK! “That’s gold, Jerry. Gold!”

Wait a second. I’ve got all the makings of a great, new, fake statistic but what about the data? What results do the formulas produce and what hypothesis can we draw these numbers? Well, I guess I should give you access to the file with the data (OpenOffice spreadsheet; PDF) I used for dRAW and RAWs. This list includes all players with a RAWs total (Runs plus Walks) equal to or greater than 100. Ninety-seven players made the cut; from a high of 237 for Jose Bautista to a low of 100 for Eric Hosmer.

Standard 5×5 scoring leagues (BA/R/HR/RBI/SB) don’t use walks in any of their scoring calculations but the idea of a high RAWs score seems to relate to a valuable fantasy baseball asset. Only six players topped a RAWs score of over 200 in 2011 and all six players were ranked within the top 16 of ESPN’s Player Rater (batters only).

Player Team Pos R BB dRAW RAWs ESPN Player Rater (Batter Ranking)
Bautista, J TOR RF 105 132 0.795 237 7
Granderson, C NYY CF 136 85 1.600 221 4
Cabrera, M DET 1B 111 108 1.028 219 5
Votto, J CIN 1B 101 110 0.918 211 14
Kinsler, I TEX 2B 121 89 1.360 210 16
Fielder, P MIL 1B 95 107 0.888 202 15

Not a bad group of fantasy baseball studs. So I guess the first conclusion I can make from this data is that a 200+ RAWs is made up of elite fantasy baseball players dominated by first basemen. NOTE: Bautista lead the majors in Walks and Curtis Granderson lead the majors in Runs, so it only seems logical they’d be at the top of the RAWs leaderboard.

The next group of players are those with a RAWs of 199 down to 170 and includes the two best fantasy baseball players in 2011.

Player Team Pos R BB dRAW RAWs ESPN Player Rater (Batter Ranking)
Kemp, M LAD CF 115 74 1.554 189 1
Pedroia, D BOS 2B 102 86 1.186 188 8
Gonzalez, A BOS 1B 108 74 1.459 182 6
Berkman, L STL RF 90 92 0.978 182 25
Santana, C CLE C 84 97 0.866 181 89
Zobrist, B TB RF 99 77 1.286 176 33
McCutchen, A PIT CF 87 89 0.978 176 37
Swisher, N NYY RF 81 95 0.853 176 83
Pena, C CHC 1B 72 101 0.713 173 118
Ellsbury, J BOS CF 119 52 2.288 171 2

Matt Kemp and Jacoby Ellsbury bookend this group of very valuable fantasy baseball assets. I was surprised to see some low-ranking players in this group because I’ve been attempting to tie RAWs to “value”. So I decided to find the average batter ranking of the 16 players in the 170+ RAWs group and came up with a 28.9 rating. So depending on your definition of “elite”, this group is holding its own so far. Be sure to take a look at the entire group of 100+ RAWs players here (OpenOffice spreadsheetPDF) and tell me what patterns you’re able to find to help validate RAWs as a useful, but very fake statistic.

Now that we’ve taken care of the easy addition, it’s time to head on over to the easy division. Calculating dRAW requires us to simply divide Runs Above Walks. We’ll be using the same criteria of needing a RAWs of 100 or greater so I wouldn’t have had to create multiple worksheets. But before looking at what this data means, I think we need to find a baseline norm because we are dealing with fractions and decimal points.

One of the most common synonyms for a Walk in baseball is a “free pass”. So looking at the Walk as a gift, shouldn’t a team be doing everything in its power to get that runner across the plate for a Run? Following that logic, I think the baseline (no pun intended) norm for this fake statistic should yield a 1.0. In an ideal world, every Walk received should equal one Run.

Now it is time to dig deeper into whether or not this line of logic actually makes sense. Once a runner reaches base via a Walk, he has little to no control over whether or not he will cross home plate before the inning is over. And does this logic penalize the player who walks fewer times than others or vice versa? Flawed logic. Gaff! Rather than give up at this point, let’s take a look at some of the data.

Of the 97 players with a RAWs of 100+, exactly one had the same number of Runs and Walks during the 2011 season. Kevin Youkilis had 68 Runs and 68 Walks for the only “perfect score” of 1.0 in the group. Twelve players in this group had more Walks than Runs, which takes their dRAW into the negative (less than 1.0). Here’s a look at that group:

Player Team Pos R BB dRAW RAWs ESPN Player Rater (Batter Ranking)
Pena, C CHC 1B 72 101 0.713 173 118
Bautista, J TOR RF 105 132 0.795 237 7
Swisher, N NYY RF 81 95 0.853 176 83
Santana, C CLE C 84 97 0.866 181 89
Fielder, P MIL 1B 95 107 0.888 202 15
Konerko, P CWS 1B 69 77 0.896 146 30
Votto, J CIN 1B 101 110 0.918 211 14
Werth, J WSH RF 69 74 0.932 143 105
Sanchez, G FLA 1B 72 74 0.973 146 91
Longoria, E TB 3B 78 80 0.975 158 70
McCutchen, A PIT CF 87 89 0.978 176 37
Berkman, L STL RF 90 92 0.978 182 25

It’s no surprise to see Carlos Pena lead this list on the negative side of the equation. He had a high Walk total and a relatively lower Run total. Same goes for Bautista. He lead the league in Walks, had the highest RAWs score but generated a low dRAW due to the the significant difference between his lower Runs and higher Walks.

Looking at the average batter ranking of these 12 players is 57.0 with an average RAWs of 177.6. Not bad at all for a group of players considered on the negative side of a baseball statistic.

So if we looked the the negative side of this statistic, what does the positive side (highest amount above 1.0) of this statistic look like? Let’s take a look at the top-10 dRAW players:

Player Team Pos R BB dRAW RAWs ESPN Player Rater (Batter Ranking)
Bourjos, P LAA CF 72 32 2.250 104 84
Ellsbury, J BOS CF 119 52 2.288 171 2
Aybar, E LAA SS 71 31 2.290 102 53
Reyes, J NYM SS 101 43 2.349 144 11
Hardy, J BAL SS 76 31 2.452 107 69
Castro, S CHC SS 91 35 2.600 126 23
Kendrick, H LAA 2B 86 33 2.606 119 55
Cano, R NYY 2B 104 38 2.737 142 13
Cabrera, M KC CF 102 35 2.914 137 17
Beltre, A TEX 3B 82 25 3.280 107 28

Adrian Beltre tops the list of dRAW rankings, scoring Runs at a rate of over three times the frequency of taking a Walk. This group of players seems to be filled with quite a bit of base stealers and a few free swingers. An interesting mix, for sure. But does it mean anything? Can dRAW help you pinpoint true fantasy value? The average batter ranking for these 10 players is 35.5. Hmmm, there’s value there for sure but is it predictable? Would you be drafting Aybar and Hardy in the same class as Reyes? Definitely not. Be sure to look at and play around with the entire dRAW ranks here (OpenOffice spreadsheetPDF) and let me know if you’re seeing trends or value in this new, fake statistics that I may have overlooked.

So where do we go from here? I think there is some fantasy baseball value to recognizing a players RAWs score and probably much more if your league uses on-base percentage in place of batting average (a common practice these days). I’m not sold much can be learned from a player’s dRAW value since the player has no control over the rate in which they score runs once they reach base yet have a lot of control over the frequency in which they Walk.

Have a comment or question to add to this topic? I’d really love to hear your thoughts on RAWs and dRAW. Is there a way to improve these statistics to give them true fantasy baseball value?

Have an idea for a fake statistic you’d like me to investigate in a future post? Leave a comment here or reach out to me on Twitter @DJAubain. I hope you enjoyed reading this as much as I enjoyed messing around with the topic of Fake Statistics.

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