For all future posts please visit my new blog:
http://www.alamarsportsanalytics.com/blog.html
Monday, November 26, 2012
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
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).
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.
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:
http://bit.ly/AENiBD
http://bit.ly/AENiBD
Thursday, September 8, 2011
Beyond 'Moneyball':
The first a a three part series I am writing was published in Analytics Magazine.
Beyond ‘Moneyball’:
We define sports analytics as “the management of structured historical data, the application of predictive analytic models that utilize that data, and the use of information systems to inform decision makers and enable them to help their organizations in gaining a competitive advantage on the field of play.”
(http://www.analytics-magazine.org/special-articles/391-beyond-moneyball-the-rapidly-evolving-world-of-sports-analytics-part-i.html)
Beyond ‘Moneyball’:
The rapidly evolving world of sports analytics, Part I
By Benjamin Alamar and Vijay Mehrotra
Over the past few years, the world of sports has experienced an explosion in the use of analytics. In this three-part series, we reflect on the current state of sports analytics and consider what the future of sports analytics may look like.We define sports analytics as “the management of structured historical data, the application of predictive analytic models that utilize that data, and the use of information systems to inform decision makers and enable them to help their organizations in gaining a competitive advantage on the field of play.”
(http://www.analytics-magazine.org/special-articles/391-beyond-moneyball-the-rapidly-evolving-world-of-sports-analytics-part-i.html)
Friday, July 8, 2011
Can Alex Smith Save the 49ers?
I recently broke the news to my son that Alex Smith appears to be slotted in as the starting QB for his beloved 49ers. I took the dejected look on his face as a challenge to provide him some hope. Going to the data on QB's is always a iffy proposition as QB stats are the product of the efforts of an entire team, not just one player and given the woeful play of the 49ers over the last few years, I was expecting that this would be a rather fruitless effort in restoring hope.
I pulled up Smith's page on Pro-Football Reference and checked on his performance. I then took his age, years in the league, Yds/Att and interception rate for last season and plugged them into PFR's useful Player Season Finder. I frankly did not believe what I found. Besides Smith, the Player Season Finder found three other players who had a similar performance in Yds/Att and interception rate during their 5th season, at age 26: Drew Brees, Peyton Manning, and Ben Roethlisberger. Looking at a broader range of stats, Smith actually fits in with this group fairly well.
On Yds/Att, QB rating, TD% (indexed by PFR), Int% (indexed by PFR), and Completion Percentage, Smith fits right in. He is better than Big Ben in TD%, QB rating, and Compl%, better than Brees in Int%, and just behind the three on Yds/Att. The other three of course all of Super Bowl rings and multiple All-Star game appearances on their resumes. So what is going on? How can the much maligned Alex Smith, playing for multiple head coaches and offensive coordinators, have delivered a similar performance as these shining examples of QB play?
To try and answer that question, I expanded my search. I removed all the filters on performance, and just looked for QB's in their 5th season while they were 26 years old. Since 1980, there are 33 QB's not named Alex Smith who played their 5th season when they were 26. After eliminating all the QB's with fewer than 8 games played, I loaded their advanced passing stats into two different clustering analyses to find groupings of players that had similar performances across all of the stats. The results of the two analysis were the essentially the same and put Smith in with the following group:
For some context, the rest of the QB's fall into the following groups:
I pulled up Smith's page on Pro-Football Reference and checked on his performance. I then took his age, years in the league, Yds/Att and interception rate for last season and plugged them into PFR's useful Player Season Finder. I frankly did not believe what I found. Besides Smith, the Player Season Finder found three other players who had a similar performance in Yds/Att and interception rate during their 5th season, at age 26: Drew Brees, Peyton Manning, and Ben Roethlisberger. Looking at a broader range of stats, Smith actually fits in with this group fairly well.
On Yds/Att, QB rating, TD% (indexed by PFR), Int% (indexed by PFR), and Completion Percentage, Smith fits right in. He is better than Big Ben in TD%, QB rating, and Compl%, better than Brees in Int%, and just behind the three on Yds/Att. The other three of course all of Super Bowl rings and multiple All-Star game appearances on their resumes. So what is going on? How can the much maligned Alex Smith, playing for multiple head coaches and offensive coordinators, have delivered a similar performance as these shining examples of QB play?
To try and answer that question, I expanded my search. I removed all the filters on performance, and just looked for QB's in their 5th season while they were 26 years old. Since 1980, there are 33 QB's not named Alex Smith who played their 5th season when they were 26. After eliminating all the QB's with fewer than 8 games played, I loaded their advanced passing stats into two different clustering analyses to find groupings of players that had similar performances across all of the stats. The results of the two analysis were the essentially the same and put Smith in with the following group:
- Tommy Kramer
- Steve McNair
- Rich Gannon
- Bernie Kosar
- Neil Lomax
- Gary Hogeboom
- Trent Dilfer
- David Whitehurst
- Randall Cunningham
- Paul McDonald
- Kyle Boller
- Tim Couch
- Ben Roethlisberger
For some context, the rest of the QB's fall into the following groups:
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