Baseball has forever been a game of statistics and over the past few decades, sabermetrics have taken this passion way beyond batting average and on-base percentage. Growing up, my family always said all you needed to do to be successful was to “build a better mouse-trap,” meaning, if you can find a way to improve on existing information, to make something more efficient, you’ll find success follows. Statistics in baseball are the new mouse-trap; for the most part, the numbers being used haven’t changed for 100 years, they are just being manipulated in a way to provide a better benchmark to evaluate a player’s value and/or worth. The end goal never changes: a better mouse-trap still kills the mouse in the end, and a better statistic still just evaluates a player, but the means or accuracy of doing so makes it special.

The problem with the evolution of statistics in baseball has been the public acceptance of them. I’d be just as willing to bet that Woodrow Wilson and his friends talked about Babe Ruth’s batting average in 1915 as I would bet that Barack Obama won’t discuss the VORP of David Ortiz in 2009. Some statistics resonate through the general public and become part of the casual fan’s conversation, and some don’t. The “stickiness” of a stat depends on how complicated it is to understand, calculate, or relate to something the average fan can appreciate.

The numbers being thrown around by stat heads these days are often hard to grasp. Even an easy concept, such as Batting Average on Ball In Play, can be misunderstood and misused, as I demonstrated during my fourth outfielder series. Okay, so we all agree that batting average is a horrible statistic to base the value of a player on, but what metric can we all agree on that makes sense? Fortunately for us, Dave Cameron over at FanGraphs, has put together an eight part series on Win Values. Click here to jump to the end of the page for links.

Win Values are used to calculate two very easy to understand numbers, the number of wins a player is worth, the amount of money those wins are worth to his team. Of course, you can take the amount of money a player is worth in terms of wins and compare it to his actual salary to determine if a player was a bargain or bust during the previous year. That is what my intention is with this new series on Win Values. Today, I am going to do a reader’s digest version of explaining Win Values, followed next week by a breakdown and analysis of the 2008/2009 teams. Trust me, if you are at all interested in this metric, please read Dave’s series instead. My version is the equivalent of the Pineapple Express guys explaining Einstein’s theory of relativity; this is very interesting stuff that I am probably going to butcher in one form or another!

There are five major components to determining a player’s Win Value worth: batting, fielding, position, replacement player value, and value of a win. Batting is the easiest to understand, with a little background information on some existing metrics that you may or may not be aware exist. First is wOBA, or weighted On-Base Percentage, which takes the on-base percentage of a player and weights it compared to the league average. In 2008, the average OBP was .332; Dustin Pedroia had a .376 OBP and therefore a .382 weighted OBP, or wOBP. There are some rather complicated formulas, detailed here, to devised the runs above average created by a player’s wOBP, called, wRAA, or, weighted runs above-average.

If you are anything like me when it comes to “new age” statistics, you are already lost or just about there. Let’s recap quickly. On-base percentage (OBP) is how often a player reaches base per plate appearance, wOBP is his OBP weighted against the league average. wRAA is a manipulation of wOBP that determines how many runs above average a player generated for his team. However, wRAA does not factor in park adjustments; it’s obviously easier to hit in Boston or Colorado compared to San Diego or Seattle, right? Forunately, Batting Win Values accounts for park adjustments to a player’s wRAA. Looking at the reigning MVP again, Pedroia had a wRAA of 32.6 in 2008, but was actually devalued by playing in Fenway, so his final Batting Win Values number is 30.4. By comparison, Ian Kinsler had a wRAA of 31.4, but playing in Arlington, a Batting Win Values of only 22.1 for the Rangers in 2008.

Calculating Dustin Pedroia’s Win Value – 2008 | ||||

Batting Value |
Fielding Value | Positional Adjustment | Replacement Adjustment | Value Runs |

30.4 | ||||

The second component of Win Values is Fielding, probably the most debatable metric in today’s sabermetric world. Fielding is very difficult to judge, but according to FanGraphs, the most reliable statistic is Ultimate Zone Rating (UZR.) For the purposes of Win Values, the fielding value for a player is the sum of all UZR from each position he played in a specific year. Pedroia’s 2008 UZR was 8.9, so mark that down as the Fielding Win Value.

The next calculation is the Positional Adjustment, used to try to “even out” all players. FanGraphs calculated a positional adjustment scale for all positions; for 2nd base, it is a +2.5 (1st base is -12.5, FYI). The adjustments are then scaled to match the games played at each position, so Dustin Pedroia, who played in 157 games, had a positional adjustment of +2.4.By comparison, Chipper Jones, who plays third base (that also has a maximum +2.5 positional adjustment), had only a +1.6 for 2008 because he missed 47 games.

Calculating Dustin Pedroia’s Win Value – 2008 | ||||

Batting Value |
Fielding Value | Positional Adjustment | Replacement Adjustment | Value Runs |

30.4 | 8.9 | 2.4 | ||

These three numbers, Batting, Field, and Positional Adjustment, create another metric: runs better than average player. For Dustin, 30.4 + 8.9 + 2.4 = 41.7 runs better than the average league player, which is really good. Chase Utley, the example used by Dave, was 58.6 runs better than the average player in 2008. To get true value however, that number needs to be compared to the run value of a replacement player, defined as an “AAAA” player; not quite good enough to start at MLB, but of higher skill than the average AAA player.

It is widely believed that a replacement player would cost a team -20 runs per 600 plate appearances, so for every 600 plate appearances a player has, he receives +20 runs as a replacement player adjustment. Basically, this is a reward to the player’s value since the team he plays for didn’t have to use a replacement player instead of him. Since Dustin Pedroia had 726 plate appearances, he receives a +24.2 replacement adjustment. JD Drew, who only had 456 plate appearances in 2008, received only a +15.2 replacement adjustment for comparison’s sake. Adding in the replacement player adjustment to the runs better than the average league player, we can now calculate Dustin Pedroia’s Value Runs (41.7 + 24.2 = 65.9).

Calculating Dustin Pedroia’s Win Value – 2008 | ||||

Batting Value |
Fielding Value | Positional Adjustment | Replacement Adjustment | Value Runs |

30.4 | 8.9 | 2.4 | 24.2 | 65.9 |

The next step in Win Values is to determine the value of runs in terms of wins, or to put it a different way, how many runs must a player score to be worth 1 win for his team. Dave uses the Pythagorean Winning Percentage logic to calculate that 10 runs = 1 win for the purposes of Win Values. The Pythagorean Winning Percentage is used by ESPN and MLB on their standings page on the “Expected Win/Loss Record.” It basically takes the Runs Scores divided by the Runs Allowed, and applies that percentage over the amount of games played. Again, for more detail on how the 10 runs = 1 win logic was manipulated from this theory, check out the FanGraphs links at the bottom of this article. Using this calculation, we can see that Dustin Pedroia was worth 6.6 wins for the Red Sox last season; newly acquired Yankees first baseman Mark Teixeira was worth 6.8 for the Braves and Angels in 2008.

The final step of calculating Win Values in terms of dollars is to figure out how much a win is worth to each team in a specific year. In theory, every dollar a team spends over the league minimum payroll is spent with the intention of achieving available wins. In 2008, the minimum payroll was $12 million a team, so $360 million for the entire league, yet $2.67 billion was paid in salaries for the year. Using quick division of the money spent above payroll divided by wins, the cost of a win in 2008 was $2.31 million.

However, this is not the true value of a win, especially when you consider players who have not yet signed extensions or were signed as free agents. Looking at FanGraph’s example, C.C. Sabathia was worth 5.5 wins as a pitcher in 2007, but if you offered him a $12.65 million contract ($2.31 million per win * 5.5 wins), you wouldn’t get too far. The market for wins needs to be considered more than the actual value of wins.

Dave determined the cost of a win in 2008 was actually $4.5 million. His formula uses the value of the free agent contracts signed in the previous year, divided by a three-year weighted average of their win values, and added in an aging and talent factor to keep things realistic. For fans like you and I, the means aren’t as important as the final number, $4.5 million per win in 2008, meaning our diminutive second baseman, who was paid $0.5 million in 2008, was actually worth $29.6 million (6.6 wins * 4.5 million per win) to the team.

It is important to note that Win Values can be misleading if only considering one season, for example, Jayson Werth of the Phillies was worth $22.0 million last year in Win Values, but won’t exactly see teams lining up willing to give him A-Rod money for the next 10 years. However, I do believe Win Values are an exciting new metric that can be discussed in terms that the common fan will understand, even if they don’t care or grasp how they are calculated in the first place. Stay tuned for my follow-up piece analyzing the Win Values of the local nine from the past season, including some of the “new blood” the front office has added for 2009.

**Dave Cameron’s Series on Win Values**

Part 1

Part 2

Part 3

Part 4

Part 5

Part 6

Part 7

Part 8

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