### Introduction

WAR means Wins Above Replacement and has become a very popular stat. Like this data model’s Wins Above Average, WAR tries to ascribe wins to players with a resulting number that can be used to rank those players amongst each other. There are several variants of this WAR stat that can differ greatly from site to site. These next few posts will examine WAR in the context of its results and not dwell on its mathematical basis or lack thereof. There are no mathematical proofs posted for any WAR variants.

In my analysis of WAR I’ll focus on results from baseball-reference.com which is an excellent site for any kind of historical baseball research. They include WAR valuations in their tables which I will use for comparison with this data model’s WAA on a player and team basis. In the end it’s results that matter. Which stat can best discern the myriad of baseball statistics into a single ranking value?

Here’s a blurb from Baseball-Reference.com WAR Explained

There is no one way to determine WAR. There are hundreds of steps to make this calculation, and dozens of places where reasonable people can disagree on the best way to implement a particular part of the framework. We have taken the utmost care and study at each step in the process, and believe all of our choices are well reasoned and defensible. But WAR is necessarily an approximation and will never be as precise or accurate as one would like.

This is not true with this data model’s WAA. There is only one way to calculate WAA and there are mathematical proofs to describe that calculation. Analyzing the results of WAR will provide for a better understanding into the math behind WAA.

### Let’s get Started

My first question from a macro level wondered about the sum of all player WAR values for each season. What did WAR add up to? I chose to limit the study from 1970-2013 which should provide a deep enough data set to figure out what they’re doing. WAR Totals 1970-2013 is a large table I made listing league WAR totals for batters and pitchers. To keep things short here are a couple random years pulled from that table:

Year |
BAT |
% |
PITCH |
% |
#Teams |
BAT/Team |
PITCH/Team |
---|---|---|---|---|---|---|---|

1970 | 472.7 | 0.59 | 328.0 | 0.41 | 24 | 19.7 | 13.7 |

1971 | 471.7 | 0.59 | 327.7 | 0.41 | 24 | 19.7 | 13.7 |

1987 | 518.9 | 0.59 | 355.3 | 0.41 | 26 | 20.0 | 13.7 |

1988 | 515.7 | 0.59 | 353.7 | 0.41 | 26 | 19.8 | 13.6 |

2012 | 598.7 | 0.59 | 409.9 | 0.41 | 30 | 20.0 | 13.7 |

2013 | 598.6 | 0.59 | 410.3 | 0.41 | 30 | 20.0 | 13.7 |

The first thing I noticed is that batters make up 60% of total league WAR, pitchers 40%. This is consistent every year. WAR combines the fielding class with the batter class I assume WAR places pitchers and batters as equal entities which would lead to a logical conclusion that WAR values fielding 20%, batting 40% and pitching 40%. A team average WAR for batting is almost exactly 20. It is unclear if someone determined that would make for a good average and worked backwards or it just somehow came out that way.

From the historical table I can tell WAR is conserved. There are only so much WAR value that can go around to each player. In WAA the sum of all pitchers and batters results in zero. With WAR, for batters in 30 team leagues, the resulting sum is 600, 400 for pitchers. Out of the 600 league total for batters, 1/3 of that, or 200 is reserved for fielding.

At least WAR adds up to something. In future posts on this topic we will determine its efficacy.