How (baseball) statistics can help you earn $2.025 million

Traditional baseball statistics would say that Pittsburgh Pirates pitcher Ross Ohlendorf didn’t have a great 2010 season: the 27-year old had 1 win against 11 losses with 108 innings pitched in 21 games. Yet, in an arbitration hearing, Ohlendorf just earned a pay raise from $439,000 to $2.025 million. What happened?

Even though this might seem like a minor matter (the average MLB salary in 2010 was $3.3 million), there is plenty of talk already that Ohlendorf benefited from statistics (and a field known as sabermetrics) that have become fairly normal in the last 20 years in baseball. Ohlendorf’s WHIP ((walks + hits)/innings pitched) was decent at 1.384. His ERA+ (comparing his ERA to the league average and adjusting for the ballpark) was 100, right at the league average.

Ultimately, these statistics suggest that Ohlendorf’s performance was decent, at least average. His main problem was that he was pitching for a terrible team that finished with 57 wins and 105 losses. With a little more data beyond what typically goes on a baseball card or is flashed on a television graphic, Ohlendorf got a sizable raise.

There could be some alternative takes on this outcome:

1. Wow, even an average MLB pitcher can make big money.

2. It would be interesting to know whether Ohlendorf’s representative in the arbitration hearing used all of these advanced statistics to make his case.

3. How quickly can workers in other careers develop advanced statistics to further their pitches for raises?

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