Can Analytics Help Resolve The Jeter Contract Flap?

Statistical analysis is now a standard tool in the sports technology portfolio. But how do you measure loyalty, leadership and other qualitative values?
The ongoing contract negotiations between shortstop Derek Jeter and the New York Yankees once again demonstrate that it's not about the numbers -- it's how you use them. That's where business intelligence and data analytics come in.

As the iconic Yankees player concludes his current ten-year contract and seeks a new one to ride out his remaining years in baseball -- in all probability, not more than 3 to 5 years -- the two parties are staring at each other across a wide chasm of expectations.

The New York Yankees are offering a shorter contract and (relatively speaking) less money -- a three-year, $45 million offer. The player expects more: four or five years, at upwards of $20 million each year.

The differences begin with perspective. Jeter's agent makes the case that Jeter cannot be valued merely in terms of statistics, e.g. his age and most recent season's performance; as the captain and the "face" of the Yankees, Jeter's contributions must be measured in totality.

On the other hand, the Yankees' president demurs: "This isn't a licensing deal or a commercial rights deal... He's a baseball player, and this is a player negotiation." Ergo, in determining Jeter's value, his recent and projected future performance is paramount.

Which brings us to the much-less-exciting world of business analytics. In analytical terms, what metrics do we use to measure Jeter's value, and how do we interpret these metrics? Do we interpret his subpar performance last season (he hit .270 as against a lifetime average of .317 and had on-base percentage of .340 as against a lifetime average of .388)? Can we simply ignore the outlier -- as we so often do when charting -- and look at the overall trend?

Can we weigh Jeter's performance against the average American League shortstop, who hit .258 and had a .312 on-base percentage last year? Could we learn from measurements of other baseball players' late-life performance against their early-life scores and then apply the learning to Jeter?

How do we aggregate these metrics to derive a single key performance indicator (KPI) that measures Jeter's "aggregate value?" In other words, how can we put together the numerous quantitative and qualitative measures -- Jeter's lifetime and recent performances, that recent Golden Glove award (despite a declining performance), his age, his iconic value to the Yankees (then again, they did let go of Babe Ruth, too), his ability to draw crowds (and thereby fill up the Yankees coffers), his "loyalty" to the Yankees -- into one comprehensive, representative KPI that can then be unitized to a dollar value?

As is nearly always the case with analytics, questions are easier formulated than answered.

The good news is that analytics is increasingly a staple in the sports technology portfolio -- as witnessed by the well-reported success of the Boston Red Sox (as analyzed by author Tom Davenport) and, more recently, the use of SAP Business Objects software in providing insight into this year’s FIFA World Cup Football (Soccer).

Statistical analysis systems like Baseball Prospectus and Marcels provide the kind of baseball metrics Jeter and the Yankees could use to determine a fair contract -- including analysis of the impact of aging on performance.

Yet getting to that aggregated KPI for Jeter's total value is a different story. At stake is a big-bucks consequence for Derek Jeter, and even larger repercussions for the Yankees, who do, after all, have a business to run -- swapping players like our children swap baseball cards.

If Jeter and the Yankees can find satisfactory (or at least mutually acceptable) answers to the questions above, the famous shortstop, who will turn 37 in June, will continue zooming along his stellar career path, and will likely be the first player to rack up 3,000 hits as a Yankee. And the Yankees organization would have hit a home run.

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