I love the Cubs. If I could, I would probably be spending each and every day sitting along the third base line at Wrigley watching the beauty of the ball in the air, listening to the crack of the bat, and hopefully seeing lots of Cubbie runs.
And baseball is a great game. It has mystical moments when underdogs triumph against all odds and teams pull back from the edge of defeat to go on miraculous winning streaks. Over the past thirty years, however, mathematicians dug deeper into the magic of the game to look for the underlying truths to what makes winning teams. Sure, some players may just be so amazing and fantastic that they single-handedly win games for their teams. But, particularly in baseball, one player does not make a team. So, led by a man named Bill James, mathematicians found a new angle to look at baseball stats called sabermetrics.
One of the results discovered was that a team's win-loss record connected strongly to how many runs they scored compared to how many runs they allowed opposing teams to have. This seems pretty intuitive. If a team can consistently score a lot of runs and regularly keep the other teams from scoring much then there is a good chance they will have a good record.
Bill James took this one step further and developed a formula for it:
Winning Percentage = (Runs scored)^2 / [{Runs scored)^2+(Runs allowed)^2]
Look familiar?
c^2 = a^2 + b^2
That's good old Pythagoras above, with the Pythagorean Theorem. Now, James's Pythagorean Theorem of Baseball is not directly a result of Pythagoras, but the two look close enough alike that James's idea got the same name. Using this formula (or a slightly modified version of it), you can fairly accurately model how a team will do for a season in terms of their win-loss record. Pretty neat!
And it doesn't stop at baseball. The Pythagorean Expectation is also applied to basketball and football. Here's the basketball formula:
Winning Percentage = (points for)^13.91 / [(points for)^13.91 + (points against)^13.91]
These are just the tip of the iceberg as far as stats go. Now that the professional sports are multi-billion dollar businesses, statisticians are constantly analyzing and interpreting data to find an edge into what makes a winning team. Pro teams, the sports leagues, and sports stats companies need many people to assemble and digest all the data that is out there. So if you have a love of sports and an interest in mathematics, a sports statistician is a great career to consider.
Now if you are partway through the season, try some of these Pythagorean-esque ideas out and see what sort of predictions you can make. You may be amazed at how accurate they turn out to be!
For more information:
Len and Bob's Blog at WGN - http://www.wgntv.com/blogs/lenandbob/wgntv-stats-sunday-if-at-first-you-dont-achieve-20120902,0,6580908,full.story
Pythagorean Theorem of Baseball at Baseball-Reference.com - http://www.baseball-reference.com/bullpen/Pythagorean_Theorem_of_Baseball
Pythagorean Expectation at Wikipedia - http://en.wikipedia.org/wiki/Pythagorean_expectation
And baseball is a great game. It has mystical moments when underdogs triumph against all odds and teams pull back from the edge of defeat to go on miraculous winning streaks. Over the past thirty years, however, mathematicians dug deeper into the magic of the game to look for the underlying truths to what makes winning teams. Sure, some players may just be so amazing and fantastic that they single-handedly win games for their teams. But, particularly in baseball, one player does not make a team. So, led by a man named Bill James, mathematicians found a new angle to look at baseball stats called sabermetrics.
One of the results discovered was that a team's win-loss record connected strongly to how many runs they scored compared to how many runs they allowed opposing teams to have. This seems pretty intuitive. If a team can consistently score a lot of runs and regularly keep the other teams from scoring much then there is a good chance they will have a good record.
Bill James took this one step further and developed a formula for it:
Winning Percentage = (Runs scored)^2 / [{Runs scored)^2+(Runs allowed)^2]
Look familiar?
c^2 = a^2 + b^2
That's good old Pythagoras above, with the Pythagorean Theorem. Now, James's Pythagorean Theorem of Baseball is not directly a result of Pythagoras, but the two look close enough alike that James's idea got the same name. Using this formula (or a slightly modified version of it), you can fairly accurately model how a team will do for a season in terms of their win-loss record. Pretty neat!
And it doesn't stop at baseball. The Pythagorean Expectation is also applied to basketball and football. Here's the basketball formula:
Winning Percentage = (points for)^13.91 / [(points for)^13.91 + (points against)^13.91]
These are just the tip of the iceberg as far as stats go. Now that the professional sports are multi-billion dollar businesses, statisticians are constantly analyzing and interpreting data to find an edge into what makes a winning team. Pro teams, the sports leagues, and sports stats companies need many people to assemble and digest all the data that is out there. So if you have a love of sports and an interest in mathematics, a sports statistician is a great career to consider.
Now if you are partway through the season, try some of these Pythagorean-esque ideas out and see what sort of predictions you can make. You may be amazed at how accurate they turn out to be!
For more information:
Len and Bob's Blog at WGN - http://www.wgntv.com/blogs/lenandbob/wgntv-stats-sunday-if-at-first-you-dont-achieve-20120902,0,6580908,full.story
Pythagorean Theorem of Baseball at Baseball-Reference.com - http://www.baseball-reference.com/bullpen/Pythagorean_Theorem_of_Baseball
Pythagorean Expectation at Wikipedia - http://en.wikipedia.org/wiki/Pythagorean_expectation