Recently, Shelley Metzenbaum, associate director of performance and personnel management at the Office of Management and Budget, blogged about Saving Taxpayer Dollars With Moneyball.

She said: “Using all the relevant data we can find to do more with less must be the rule, not an exception, in government.”

She went on to say, “Of course, statistics are not everything. A manager needs to know how to use them – how to ask the right questions, to allocate resources and negotiate agreements, and when to address a program or staff member that looks good on paper but is not delivering results. Digging into data is both an art and a science that, admittedly, sounds pretty geeky. But it’s critical not just for winning pennants, but for delivering for the American people.”

In a recent story, I mentioned that Bob Flores, former CIA chief technology officer, also used the recent movie “Moneyball” as an example of how the new field of baseball analytics called Sabermetrics has shown there is no more rigorous test of a business plan than empirical evidence. “Moneyball” the movie, is based on the 2003 book by Michael Lewis.

Sabermetrics, and predicitive analytics, as it turns out, offer an interesting set of approaches in analyzing workplace performance, including in government.

Sabermetrics, according to Wikipedia, is the specialized analysis of baseball through objective, empirical evidence, specifically baseball statistics that measure in-game activity. The term is derived from the acronym SABR, which stands for the Society for American Baseball Research. It was coined by Bill James, who is one of its pioneers and is often considered its most prominent advocate and public face.

James defined sabermetrics as “the search for objective knowledge about baseball.” Thus, Sabermetrics attempts to answer objective questions about baseball, such as “which player on the Red Sox contributed the most to the team’s offense?”

Statistics play an important role in summarizing baseball performance and evaluating players in the sport. An extensive list of commonly used baseball statistics can also be found in Wikipedia.

Predictive analytics is another area of statistical analysis. It deals with extracting information from data and using it to predict future trends and behavior patterns.

The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.

One of the most well-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. A well-known example would be the FICO score. The letters stand for Fair Isaac Corporation.

When most baseball fans look up statistics of their favorite players, they usually look for the ‘big 3’ of batting average, home runs and RBI’s. These 3 statistics are the standard measure of individual offensive performance, but don’t really measure how they contributed to their own team’s success.

That’s why new statistical measure, “Win Shares,” was created to determine how individual performance contributes to team success.

Win Shares takes into account other contributions a player makes to a game besides batting average, home runs and RBI’s. Very often you will find players who are not league leaders in any of the ‘big 3’ stats, but are among the league leaders in Win Shares…making it a seemingly more meaningful measure.

For additional insight into baseball statistics, and the concepts behind the statistic Win Shares, look at New Bill James Historical Baseball Abstract by Bill James. It took James more than 100 pages in the book to explain his formula so I will not go into that here, just show the results.

I thought I would illustrate all of this for our readers with simple example of exploring some relationships in baseball statistics for 2005 for 278 players on 30 teams that I found on the Web to see what they said and if they could be used to predict what happened in 2011. The interactive details are presented elsewhere. I found there was a positive correlation between Win Shares and Salary and Home Runs, but quite scattered.

In 2005, the highest paid player (Alex Rodriquez-$26,000,000) had the second highest number of home runs (48) for a Win Shares of 37 (second highest). The player with the highest WinShares (Albert Pujols – 38) was paid less than half ($11,000,000) of the highest paid player. Fast forward to 2011 – Albert Pujol’s team (St. Louis Cardinals) won the World Series and he was selected as the MVP, while Alex Rodriquez’s team (New York Yankees) were eliminated in the playoffs.

Could one have predicted that outcome? Probably not from the past statistics, but my intuition tells me that I should not be surprised because Albert Pujols might have had a stonger incentive to win because he was already performing at the Alex Rodriquez level but not getting paid for it so he had more to prove.

Sports, and all of life’s endeavors, are usually more about motivation and perserverence (“heart as they say”) than statistics and predictive analytics. The same might be said about how government employees perform.