What is Win Expectancy?

Win Expectancy Definition

Win expectancy is the odds of each team winning at any given point in the game. The odds for each game start at 50/50 and shift according to the score, inning, base-out state, and run environment. 

The number expressed as win expectancy is always in reference to the home team, so saying that the win expectancy is 60% means the home team has a 60% chance of winning the game. 

The Concept of Win Expectancy

The concept here is that the odds of a given team winning a game can be calculated using the current game state and historical data regarding what has happened previously when teams have faced the same situation. 

How often will a team win when they are trailing by two runs in the bottom of the 7th inning, with the bases loaded and one out? You might be able to guess just by using your experience as a baseball fan, but you couldn’t nail it down accurately. With win expectancy, you can. 

Relying on data from tens of thousands of previous games, the win expectancy model can produce reasonably accurate odds for each team to win at any point in a game. 

Why is Win Expectancy Important?

Using win expectancy is a great way to put a developing baseball game in context. When monitored in real-time, this stat can help you understand which moments in a game were the most important for either team. 

For instance, you already know that hitting a grand slam is a huge play for the offensive team, but how much did that home run improve their chances of winning? Win expectancy can answer that question. 

The other reason win expectancy is important is that it is the main component in the Win Probability Added stat.

How is Win Expectancy Calculated?

Calculating win expectancy is not something that you would do on your own. Not only would you need access to a wealth of historical data, you would also need to do the calculations quickly during a game. And, as soon as you were finished with one calculation, the game state would change and the math would need to be done all over again. 

A better option is to follow along during a game using the FanGraphs Win Probability page. This page monitors every game and adjusts the win expectancies accordingly as the games progress. 

An Example of Win Expectancy in Action

One of the wildest games of the 2019 season was played between the New York Mets and Washington Nationals on September 4th. Any observer would be able to tell you that this was a thrilling game, but using win expectancy gives us a clear picture of just how unlikely it was. 

Let’s walk through some key moments in the game, along with the win expectancy at that time: (Note: The Nationals were the home team, so all win expectancy numbers are expressed from their perspective.) 

  • In the bottom of the 3rd inning, with the Nationals leading 1-0, Asdrubal Cabrera walked to put runners on first and second with no outs. 
    • Win expectancy = 75%. 
  • In the top of the 4th inning, Joe Panik hit a two-run home run to move the Mets ahead 4-1. 
    • Win expectancy = 19.5%. 
  • In the top of the 8th inning, Jeff McNeil hit a solo home run, making the score 5-2 in favor of the Mets. 
    • Win expectancy = 6.9%. 
  • In the bottom of the 8th inning, Juan Soto hit a two-run home run, bringing the Nationals within a run at 5-4. 
    • Win expectancy = 30.4%. 
  • In the top of the 9th inning, Pete Alonso hit a two-run home run, pushing the Mets ahead 10-4. 
    • Win expectancy = 0.3%. 
  • In the bottom of the ninth, Ryan Zimmerman hit a one-out, two-run double. The Mets lead was cut to 10-8. 
    • Win expectancy = 28.9%. 
  • The next batter, Kurt Suzuki, hit a three-run home run, giving the Nationals an 11-10 victory. 
  • Win expectancy = 100%. 

What are the Problems with Win Expectancy?

The notable problem with win expectancy is that it does not take into account the quality of the players on the field. The model used to produce the odds of each team winning the game simply relies on historical data to determine how frequently a team in the same situation has won in the past. 

That means, for instance, the best team in the league would be given the same odds of pulling off a comeback as the worst team, which is obviously not accurate. 

Win expectancy still maintains value for the reasons discussed above, but this lack of context does limit its usefulness to a degree.