Jonathan Lucroy had Brewer fans excited with his bat early in the season. For much of the first half, he was maintaining a batting average above .280, supplying some decent power, and giving the team a semi-reliable bat out of the catcher position, something that hasn’t been since B.J. Surhoff was around. Over the second half of the season, however, it’s become apparent that Lucroy isn’t really at Surhoff’s level with the bat, as the 25-year-old has managed a mere .242/.295/.343 line since the All-Star break.

Still, Lucroy owns a .263/.311/.384 line on the season, and while that isn’t pretty, it’s good for a 92 wRC+, above the league average for catchers. He enters play Thursday with 1.7 fWAR in 451 plate appearances, slightly above the average player. According to WAR, he isn’t a star, but he is a slightly above average player — exactly what the Brewers needed.

WAR isn’t perfect, however, and one of it’s more major imperfections lies in the realm of catcher defense. Whereas some of the imperfections of the WAR come from questions with existing data (see UZR and other fielding metrics), when it comes to catcher defense, there is largely an absence of data. Currently, fWAR takes catcher defense on stolen bases into account, but that leaves a large part of the catcher’s job unquantified and assumed as zero — this clearly isn’t the case, but there’s also no reason to attempt to quantify what you can’t.

But people are still working on objective measures of catcher defense, and yesterday at Baseball Prospectus, Mike Fast published an excellent study on one aspect: framing. The entire study is worth a read, as it’s extremely well done and very detailed. The crux of the study, though, lies on the idea that framing is a very big deal. Over 150 games, a catcher can potentially save his team up to 35 runs just by making borderline pitches look like strikes instead of balls.

According to Fast’s study, that 35 number belongs to Jose Molina, an unsurprising name to see atop a defensive catchers list. Second place, on a per game basis? That belongs to Lucroy, who has saved the Brewers a shocking 38 runs since coming up in 2010, for a total of 24 runs per 150 games. Throw that into his fWAR, and he’s a 4-win player, roughly an all-star level.

Don’t take these numbers as gospel. Read the study, understand why these numbers make sense. But there’s a reason the Brewers felt comfortable going with Lucroy as their starting catcher in 2010, and it wasn’t because of his offense. They felt excessively confident about his ability as a receiver, and now, we have the numbers to back it up.

Every year, Tom Tango runs a survey called the Fan’s Scouting Report to assemble the knowledge of you, the people who inhabit this fine space we call the internet. This tool is interesting if only to see the opinion of the fans, but also, thanks to the idea of “wisdom of the crowds,” it can be a good supplement to fielding data. So please take some time and fill this out for the Brewers and any other team you follow sufficiently.

http://www.tangotiger.net/scout/

Not everybody is familiar with the win probability graph I included as part of yesterday’s game recap. As Samir summed up quite succinctly in the comments, the graph shows each team’s likelihood to win the game after each play. Basically, people with much, much better database and analytic skills than me compiled data on thousands of games, and based on the score and state of the inning (how many runners, how many outs) and the way games have historically played out, we can say what chance each team has to win the game assuming that each team is a league average team. That assumption is key – at the start of the game, we say that the Yankees have a 50% chance to beat the Orioles, even though the probability is probably closer to 60% in favor of the Yankees. Because of that assumption, though, we can derive a statistic called WPA, or “Win Probability Added,” to show how much better players and teams are than the average player.

For more on the subject, I recommend this article by The Hardball Times’ Dave Studeman.

For an excellent example of Win Expectancy and WPA in progress, check out this article by one of the leaders in Sabermetrics, Tom Tango. It takes you through the Bartman inning play by play, so even if you already know everything you want to know about WPA, it’s a fun read just because it allows us to relive the awesome Bartman game.

I’m a sabermetric writer, so I will be using some advanced statistics. First, here is the Glossary from FanGraphs, which is a good starting point. If you would like to learn some more of these statistics and concepts relatively quickly and easily, here are two excellent resources that have appeared in the blogosphere this offseason.

By Graham MacAree, one of the masterminds behind www.statcorner.com, his Sabermetrics 101 series over at Lookout Landing, an excellent Seattle Mariners blog.

By Steve Slowinski, another excellent course on Sabermetrics.