How does wins above replacement work

Then, using league averages, it is determined how many wins a pitcher was worth based on those numbers and his innings pitched total. But all three stats answer the same question: How valuable is a player in comparison to replacement level? WAR quantifies each player's value in terms of a specific numbers of wins.

After that, you simply take that sum and divide it by the runs per win value of that season to find WAR. While the sabermetric community very certainly would not pick me to represent, defend or explain any metric or stat, I am at least open to looking at WAR. And to sometimes using it in stories. Some writers, broadcasters and analysts use WAR as if everyone in the audience completely understands it. They lose some fans and can appear to be talking down to others.

It seems to me it took fans a long time for an important stat like on-base percentage to get common use and now OPS is used almost as much as any other so-called traditional stat. Chris Davis recorded 0. Sometimes it can be hard to have a debate on a stat or metric like WAR. All-Star J. Realmuto , meanwhile, has separated himself into another tier.

His 4. That leads me to another issue—WAR is not completely thorough. Realmuto is among the Marlins leadership. I still recommend using this statistic, but proceed with caution. It can be used as a predictive tool for teams and fans to try and determine what the future holds. But consider that baseball is not an automatic numbers machine. There are a lot of human factors like leadership, player development, injuries, and mental health that contribute to the results.

Cookie banner We use cookies and other tracking technologies to improve your browsing experience on our site, show personalized content and targeted ads, analyze site traffic, and understand where our audiences come from. By choosing I Accept , you consent to our use of cookies and other tracking technologies. As we mentioned, we made a choice, and that left us with an unattainable deadline to finish everything — you know, priorities and all that.

This topic has had books written about it, most teams use something that resembles WAR to a certain degree, and there are many that know far more about this subject than both of us. In the field of hockey statistics, the idea of a metric being repeatable or predictive is one that has become foundational.

In our opinion, the main reason Corsi shot attempts caught on and became such a fundamental idea in hockey work was due to its ability to better predict team wins. Expected Goals used this concept as well Sprigings xG explainer. These concepts are crucial in how we value a lot of aspects of the game, how we weed out and deal with luck, how we place confidence in players and teams for evaluation, the list goes on and on… But how does repeatability and predictiveness fit into Wins Above Replacement in hockey?

In their introduction to this series, Colin Wyers described several of their goals with their new WAR model. This was the third goal:. Almost every one of the 5 goals Wyers lays out in this series part 1 is in line with how both of us feel about WAR for hockey. While not every WAR model or similar single number method developed so far in hockey has strayed from these ideas or concepts that the major public baseball WAR models hold, the vast majority of them have. Dave Cameron discussed this in a post on Fangraphs two summers ago when the new Statcast data started arriving.

The prior WAR models in hockey have focused on prediction and true-talent evaluation. We, however, wanted to dial that back a little bit and create a model more in line with those defined in baseball. The public WAR models in baseball are inherently descriptive — they measure what a player did ; how a player added value or contributed to their team in a given span of time in a way that directly ties back to what wins games runs.

This idea is one that goes against many conventions in the hockey statistics community, but at its core, WAR is a descriptive metric. Given this fact, we were faced with a choice with the construction of our model: what do we do with this? This is definitely problematic.

Prior WAR and single number metrics in the NHL were and are , for the most part, concerned with their ability to predict future performance or evaluate true-talent. Here are our goals with this model:. This is not a bad thing — it actually might be the better option given the amount of luck and variance that occurs in hockey. The great thing about WAR is that it is a framework — there is no single correct version.

Parallel models, especially descriptive vs. We might even make another version in the future that looks nothing like this! Please take some time to read over what is referenced here. We feel understanding the history, theory, and philosophy of WAR is quite important.