Monday, November 26, 2012

New Blog

For all future posts please visit my new blog:

Thursday, August 9, 2012

Interested in a Career in Sports Analytics?

I have had a lot of conversations recently with two groups of people: 1) students/recent grads who would like to work in the field of sports analytics, and 2) executives in pro sports that are looking to higher students/recent grads who would like to work in the field of sports analytics.

In an effort to help facilitate these two groups finding each other, I would like to start a database of prospective sports analytics professionals so that when I get the call from an executive, I can send them a couple of names quickly - without relying on my memory of past conversations.

If you would like a career in sports analytics, please send me the following:

1) Resume
2) Description of your skills and accomplishments in the three areas of sports analytics (data management, analytic models, and information systems). Feel free to include a sample of your work.
3) The sports you would like to work in.

Once I have your materials I'll review them and may contact you when there is a potential fit - but no promises.

Email your materials to quantsports at gmail dot com

Monday, July 23, 2012

Inter-Decade Competitions

A favorite game of sports fans, and one that Kobe has stirred up recently, is to imagine two dominant  teams from different decades playing each other a conclude that one is better than the other. These debates are rarely about which team would actually win a game between the two, but rather which team is more significant or beloved by a particular group of fans. If these debates were actually just about who would win a game the answer would be easy: the more recent team (given enough time between the existence of the two teams) would win, no question. Athletes get bigger, stronger, and faster every year, and coaches have seen and solved the strategic innovations that previous teams have used to gain a competitive edge over their opponents.

We accept this as fact in sports where there is an objective measure that allows for easy comparisons over time. The Men's 100M sprint is one such event, and few if any fans, no matter how much they loved Carl Lewis, would suggest that he could beat Usain Bolt. We have the two best times for both men and Bolt's 9.57 is 3% faster than Lewis' 9.86 - no debate needed.

Baseball, basketball, and football have no such easy measuring stick. There are a fixed number of games that can be won and all performance statistics are produced by an offense against a defense, and the athletes and coaches on both sides are getting better. So instead we have endless debates on whether the 1985 Bears could beat the 2007 Patriots.

Some fans, having seen (and suffered through if you weren't a Bears fan) vividly remember how suffocating that defense was, how they could completely demoralize an opposing offense. Having seen such dominance, these fans will argue that no offense would stand a chance against it. By my stated rule above, however, I am clearly going to argue that the Bears would not stand a chance on the same field as the 2007 Patriots. As these types of debates have been going on since the beginning of sport ("Sure, this Sephes the Gladiator is pretty good, but did you ever see Spartacus, now he was a gladiator."), I am not going to be able to prove this to you beyond a shadow of a doubt, but I will offer some evidence.

Let us begin with remembering that dominating defensive line of the '85 Bears: Hampton, McMichael, Perry, and Dent were all outstanding players that season, and as a group, they averaged 281 pounds. The offensive line, while not as celebrated, included some very good players, and had an average weight of 274 pounds. Now weight is not by itself a determining factor in line play, but it certainly helps, and those lines were not small by the standards of 1985. The 2007 Patriots however, as the table below shows, were much bigger. The Pats offensive line averaged 306.4 pounds (11% more than the Bears O-line) and the Pats defensive line (even including Adalius Thomas a Linebacker  who was at times a defensive end) had an average weight of 296.3 (5% heavier than the Bears D-line).

The size advantage for the Patriots on the offensive line is one indicator of  why the Pats would win a game played between the two dominant teams, but it does demonstrate that athletes get bigger, stronger, and faster, and the 1985 Bears would simply not be able to manhandle the 2007 Patriots the way they did the 1985 Patriots.

The important issue for fans though is just because the 2007 Patriots would win the game, does not diminish the 1985 Bears anymore than Bolt's impressive times diminish Carl Lewis' amazing performances. The '85 Bears defense will always be one of the great defenses in the history of the league, it just would not be able to handle the offenses after 20 years of athletic and strategic evoloution.

Thursday, March 1, 2012

Communication, Leadership and Analytics

In the recent series on the future of sports analytics that I coauthored with University of San Francisco Professor Vijay Mehrotra (Part 1, Part 2, and Part 3) we wrote about the importance of communication and leadership. The importance of communication and leadership in the success of an analytics program was made abundantly clear through the results the Sports Analytics Use Survey that I conducted over the last several months. 27 individuals representing teams from the National Football League (NFL), Major League Baseball (MLB), National Basketball Association (NBA), and the English Premiership League (EPL), answered questions on their teams use of sports analytics. The survey provided significant insight into how teams are utilizing analytics and some of the problems that they are running into. It also provided an interesting example when two executives from the same team answered the survey. One of the executives was in the personnel department and the other was in the information technology (IT) department. This is a team that has clearly made some investment in analytics, and the personnel executive was clearly interested in how sports analytics could help his team gain a competitive advantage. 

An examination of the responses from these two individuals demonstrated that, even teams that are interested in developing an analytics program can end up not fully leveraging their investment if the lines of communication between analysts and decision makers are not wide open. These two executives, working for the same, relatively small organization had radically different views of the state of their team’s analytics program. The table below contains some of their responses and the conflicts are obvious. The two executives had very different ideas about how data is used and accessed within the organization.

 Either the IT executive was wildly optimistic about the state of the team’s use of analytics, and/or the personnel executive was simply unaware of the capabilities of the team. In either case though, what is clear is that integration of the analytics program into decision making was not happening. The team had not leveraged their analytic investment into a competitive advantage in part because, as these responses demonstrate, these two areas of the organization did not communicate. Lack of communication around basic concepts such as whether quantitative information has had a significant impact on the decision making process, indicates that the organization did not have a clear plan for how to utilize the tool of analytics. This was made very clear by their responses to the statement: “Your analytical capabilities are stronger than your competitor's.” The personnel executive answered “Somewhat disagree” while the IT executive answered “Strongly agree”. This extreme difference in opinion is a symptom of missed opportunities to gain a competitive advantage.

No matter how insightful or complex analysis is, it is totally wasted if it is not communicated effectively and in line with a clear analytics plan established by leadership.

Saturday, February 25, 2012

Beyond 'Moneyball': Part 3

Professor Vijay Mehrotra and I wrap up our three part series in Analytics Magazine with this: