Football Inequality

The 2010 NFL season is off to a mathematically interesting start.

The Jets, Patriots, Bengals, and Ravens each have played two games against other teams in that group of four.  The results can be organized like this (for example, the Bengals lost to the Patriots but beat the Ravens)

AFC East InequalityNow, if we interpret “wins the game” to mean something like “is better than”, and if we believe that “is better than” is a transitive relationship (i.e. “If A is better than B and B is better than C, then A is better than C”), then the Jets are better than every team in their division, including themselves!  Some might prefer to say that they are even worse than themselves.

It’s not easy producing mathematically consistent ranking systems, but it’s an interesting and useful problem, and the field is quite rich.

More on Kovalchuk’s Contract

kovalchuk 2The National Hockey League has approved the new contract between the New Jersey Devils and Ilya Kovalchuk.  As discussed in an earlier post, the NHL voided the initial contract between the two parties, essentially on the grounds that it violated the spirit of the league’s salary cap rules.

The Devils originally signed Kovalchuk to a 17-year, $102 million contract.  By the NHL’s salary cap rules, this would have counted as 102/17 = 6 million dollars per year against the team’s salary cap (their yearly spending limit on players).

However, it was fairly clear from the structure of the deal that neither side expected the final five years to be played out.  Kovalchuk was to earn the league minimum for those five years, and he would have been in his 40s.  So the league viewed this really as a 12-year, $98 million dollar deal, which should count 8 million dollars plus per year against the team’s cap.

Through clever accounting, the team had created an extra $2 million per year in financial flexibility, but the league saw the matter differently.  The league, team, and player eventually compromised on a 15-year, $100 million deal (a 6.67 million dollar cap hit), and some changes have been made to the league’s salary cap policy so problems like this won’t arise in the future.  Until the next loophole is discovered, anyway

Football Economics

This is a nice, short profile of David Romer, an economist and lifelong sports fan who briefly turned his attention to football some years ago.

http://www.nytimes.com/2010/09/05/sports/football/05romer.html

In 2002, Romer wrote the first serious academic paper asking the question “When should football teams go for it on 4th down?”, applying rigorous analytical from economics and mathematics.

belichickHere’s the simple summary:  a touchdown in football is (usually) worth 7 points, and a field goal is worth 3 points.  A team will often face the situation that, on 4th down, they can either kick a field goal with a relatively high probability of success (say 80-90%), or they can go for it on 4th down (which has something closer to a 40-50% success rate) and continue to try for the touchdown (not a guarantee).

Romer’s conclusion was basically that teams should go for it on 4th down far more often than they do.  This is essentially an expected value argument:  if, by going for it, you get 7 points about 40% of the time, that’s an average of 2.8 points per attempt; if, by kicking the field goal, you get 3 points about 80% of the time, that’s an average of 2.4 points per attempt.  So in the long run, going for it will produce more points.

However, the fact is that teams rarely go for it on 4th down, usually only trying this strategy in desperate times.  So what account for the difference between the theoretical conclusion and the practice of professionals?

Fluctuating Batting Averages

When Miguel Cabrera came up to the plate in the fifth inning of last night’s Tigers-Rays game, he was 0-for-1 in the game and his up-to-the-minute batting average was announced as .349.  I found this strange because, when the game started, Cabrera’s batting average was .350.

A player’s batting average is equal to  (total hits) / (total at-bats).  Thus the effect of one more at-bat without a hit dropped his average by .001, or 1/1000 (Note:  rounding probably plays an important role here).

I wondered if this information uniquely determined both Cabrera’s hits and at-bats this season.  Or maybe some combination of mathematics, baseball knowledge, and guessing could help me get those numbers.  I did get the numbers–unfortunately, they were wrong.

An interesting question here is “What is the smallest possible number of hits such that one more hitless at-bat results in one’s rounded batting average dropping by .001?”

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