Modern baseball’s origins are something historians don’t have a good read on. If you look at the Origins of Baseball article on Wikipedia, you’ll see that we don’t know very much about where the rules came from, but it formalized somewhere around 1845 when the Knickerbocker Club of New York City began to play baseball against the New York Nine. In 1857 16 clubs finally sent delegates to a convention to standardize the rules and standardize America’s Pastime.

Baseball statistics have their own story. A fellow named Henry Chadwick was the first to start using statistics to judge a player’s performance. It was a few years after the sport was invented and formalized that this young journalist would give himself the goal of creating “numerical evidence as that would prove what players helped or hurt a team win.”

It took nearly 100 years for baseball statistics to make it to the common man and woman. It wasn’t until 1951 when a researcher named Hy Turkin published the Encyclopedia of Baseball that used a computer to compile statistics for the first time. It wasn’t until 1977 that decent, predictive and objective statistical methods called sabermetrics were invented and distributed. Sabermetrics are the analytical methods that Theo Epstein used to break a curse and build a World Series winning Boston Red Sox. He even hired Sabermetrics’ inventor to work for the Red Sox.

It took over a century to invent and distribute the objective performance measuring statistics we use today to evaluate baseball players. To tell whether or not Greg Maddux is a better pitcher than Pedro Martinez.

But though Congress has been around for nearly 234 years we still don’t have an objective way to tell whether or not my namesake, Henry Clay was as effective of a speaker as Nancy Pelosi. This isn’t to say we haven’t been making up our own statistics. We’ve been compiling subjective scorecards for years. But we need a process of standardizing our statistics, publishing how they’re calculated, and we need to build a system for authenticating and delivering those results.

It took us over 100 years to get good baseball stats, but this isn’t to say that the data didn’t exist or wasn’t being recorded. The data was still there. Here’s the full stats on the 1876 Chicago White Stockings. People were watching, keeping score, logging the games and recording the data and even making their own statistics out of it. But it was the process of standardizing the statistics, publishing how they’re to be calculated and sharing the results that made these subjective metrics effective.

So what metrics out there are effective in evaluating our legislators? Off the top of my head, here’s some elementary ones:

1. Attendance Percentage: The percent of time a member attends Congress when it is in session.

2. Vote percentage: The percentage of time a member has voted when they’ve had the opportunity to do so.

3. Sponsored Bills Per Term: The average number of bills sponsored and co-sponsored per term

4. Sponsored Bills Passed Per Term: The average number of sponsored and co-sponsored bills passed per term

5. Party percentage: The percentage of time the member votes with their political party

6. Vote Victory Percentage: The percentage of time the member votes with a bill that passes.

These are just obvious building blocks of a much more sophisticated statistical system. All these statistics exist right now. Sunlight’s partner, Open Congress and GovTrack.us and many more track their congressional statistics in their own way as do many others. In order to do it right we need:

1. Standardization: We need to be calculating these things and naming these statistics the same way everywhere.

2. Comparison: Statistics are not relevant unless they’re in context. We need to be able to create a ranked 1-535 list for every statistic we standardize and create

3. Adoption: They need to be adopted and as pervasive as RBIs and ERAs.

4. More: We need more statistics made from the data that congress generates that provide “numerical evidence” about the effectiveness of congress. We need our own “sabermetrics” that objectively evaluate whether or not a Member of Congress is a effective at representing those that chose to elect them. The ones I’ve listed aren’t even close to being accurate predictors.

So let this be a post to start a discussion amongst the transparency community about how we can begin standardizing our own objective statistics, making them useful and centralized. Let’s start working together to invent new statistics for how our members can be evaluated.