Sunday, March 27, 2011

Business Intelligence on YouTube

After reading Freedarko exhort the wonders of YouTube and what it has done for basketball, I thought it might make sense to see what YouTube has done for analytics/business intelligence. What I learned is that a lot of companies like to advertise their products on YouTube and that some folks have no idea how to relate their quality audio content to meaningful video content. I also learned that there are some interesting BI related videos available on the web. Here are a few:

Saturday, March 26, 2011

The Undisputed Guide to FREEDARKO

Sitting on my sofa with a beer next to me and the battle for a seat in the Final Four of college basketball unfolds seems to be as near as an ideal setting for writing a review of Freedarko's Undisputed Guide to Pro Basketball History as it gets. Before discussing the book though, I must explain why a review of the book is appropriate on a blog devoted to analytics, I mean no where in their 223 pages do they even give so much as a shout out to Dean Oliver and the only advanced metric they utilize is the Jazz-O-Meter. The motivation is to remind the analytic minded among us, that there are elements of the sports we analyze that we can't get at with our metrics. There are stories that lie outside of our experiences and a richness to the sport that can be forgotten when we focus solely on the data.

My adventure with the Undisputed Guide began on my trip to the Sloan Sports Analytics Conference earlier this month. As I was packing, I look at the ever growing stack of books that I need/want to read and whittled it down to two choices: The Book of Basketball: The NBA According to The Sports Guy or FreeDarko Presents: The Undisputed Guide to Pro Basketball History. Deciding which hardcover book to slip into my computer bag was an easy choice, as Simmon's book would have required me to pack my back brace along with my computer, while FD's slim 223 page tome was significantly more portable. Once the decision was made and I found myself taxing for takeoff, I opened it up to soak in the knowledge.

What I got was an entirely unique view into the history of professional basketball. Starting with the inception of the sport, the FD team brings the reader from the barnstorming years (as a side note, some day there has to be a movie that features a game between the All American Red Heads triumphing of over the Terrible Swedes, has to happen) to LeBron with a unique point insight, humor, and totally original visualizations.

The visualizations are truly the key that separates  FD from the rest, not just because of what each individual graphic communicates (ex. 20 years of draft history color coded for college experience and post-draft value) but how their inclusion drives home the point that what FD provides is the color. I am an analyst and I live in the data. FD brings the game outside of the data and reminds me that it is not just about delving deeper and deeper into the data to find the right answer, but that what we often refer to as "noise" is actually full of great stories, if not definitive answers.

One near perfect example from the book of this is the chapter on the ABA that is subtitled "What the Hell Was the ABA?". This is a provocative question, and it largely goes unanswered, mostly because there is no definitive answer. The ABA had a profound effect on the NBA in terms of salary and style of play, but it was also fueled expressly by business men looking to cash in. Large contracts were mirages and while the league was often at the cutting edge of marketing, they could also be as hokey as it can be. As an analyst I am used to asking questions and delivering the best answer that I can. FD presents a host of interesting questions, and reminds us that some of them are so mutli-dimensional, they really don't have answers.

After reading a serious of analytics and related books, settling back into the wonders of sport is a welcomed reminder of my original motivation for being involved in sports: the fun of it all.

Differentiating Sports Analytics

I had a conversation recently with USF analytics professor Vijay Mehrotra that resulted in Prof Mehrotra's column in January issue of Analytics Magazine. As part of this conversation, I discussed with Vijay the general issues that arise when trying to implement an analytics program in a sports organization. After sharing my experiences, Vijay shared his wealth of experience in logistics and analytics in a variety of business sectors and we were both struck by the similarities. A very common story in sports and business in general, is for leaders in a business with a wealth of domain knowledge or faced with being told by people who have not have near the experience or "feel" for the industry how to change the way they operate. Understandably, domain experts are hesitant to embrace insights and new management styles from those that have not had the same deep experiences in the industry that they have.

This story in sports of course is a coach or general manager being told how to change their strategy or which players to add to their roster based on a series of calculations that they may or may not understand. For a business leader, the story is a CEO being told that their past "gut" level decisions have often been wrong and that they should change how they view their industry by looking more closely at information that is the result of a series of calculations that they may or may not understand. Clearly the data is different and the techniques used may be different, but there are many overlapping principles in how to both technically and culturally implement a strong analytics program in sports and in other industries.

The three elements of analytic systems (data management, predictive analysis, and information systems) are the same in sports and other industries. Many of the cultural hurdles are also the same, so is sports analytics a specialty within the general field of analytics or is it a field unto itself that draws on a similar set of tools? The answer is yes. Sports analytics is simultaneously a specialty within the broader field and a unique field that draws on a similar set of tools.

Explaining how sports analytics can occupy that space, lets explore why it is important to understand the uniqueness of sports analytics as a field. An analytics program has the highest probability of success when, going in, all parties involved have an understanding of the possibilities and value of sports analytics. If sports analytics is viewed as either a specialty or as completely distinct, some of the potential value of the program can be reduced.

Sports Analytics as a Sub-Field
When sports analytics is viewed as a specialty within the field of analytics, there is a tendency to seek out experts in analytics and ask them to build a product for a sports organization. The basic problem with this approach, is that consultancies that specialize in building analytic systems such as Accenture, have built their businesses by building systems that help managers maximize revenues. This can take many different forms and different industries have their own components, divisions, and peculiar industry structure, but they are all focused on profits. While I would never argue that professional sports teams are not trying to maximize profits, the sport side of the organization tends to be evaluated not on a profit basis, but on a wins basis.

Coaches that win keep their jobs regardless of how profitable the larger organization is, general managers who build consistent winners become presidents of the organization, regardless of their ability to manage the business side of the team. Therefore, the analytic systems that get produced for the sport side of the organization need to be focused on maximizing wins, and the difference between objectives (wins and profits) is not trivial.

Wins vs. Profits
Wins are a fixed resource. In the NFL for example, there are only 256 games and each team plays only 16 games. A win for one team means a loss for another. Each game is also a binary event, you either win or you lose. This creates a very different set of of objectives and manner of evaluation. In business, a project can be evaluated on a clear and consistent basis: does the investment in the project have a expected return on the invested resources that is high enough. On team, the question is not what kind of return on invested assets does an activity have, but rather can this activity increase the odds that the team wins one more game? The binary nature of the payoff has a significant impact on how information is used, the types of questions that get asked, and the impact that information can have.

Type of Information
The other major difference between sports and most businesses is the type of information that is used. Even the least analytic CEO has read a P&L, they have seen and used quantitative information  on a regular basis through their careers. While coaches and GM's have certainly seen box scores their entire lives, the information that is far more important to them is the film. Coaches review film for tendencies and strategic information and play personnel folks watch hours and hours of film to evaluate players. This creates a unique situation in which the best analytic systems need to incorporate film as an additional type of data and that any new metrics that are developed need to jibe with what the decision makers see on the film. In once respect the analytic systems serve as a mechanism to help the decision makers more efficiently process the film that they see (ie point them to the most important and descriptive film).
Sports Analytics as Separate Field
The thoughts above may suggest that sports analytics is its own unique field and is only superficially connected to the application of analytic tools in business. This of course overlooks the commonalities of the fields and how these commonalities can be utilized to increase the effectiveness of both fields. The danger for the fields is that they, like information in many organizations, become siloed and the advances in one field never get leveraged to advance the other. While the data and objectives may be different, there is significant overlap in many of the tools and techniques used throughout the process.

Data visualization, for example, is a rapidly developing field and there is significant experimentation going on in business, academia, and sports. Feedback from end users will be gathered on the effectiveness and utility of various techniques in all areas, and only if the fields are connected, will these advances be rapidly deployed.

So in the end sports analytics is the same as regular analytics, but different.

Wednesday, March 23, 2011

Impact of defensive pressure, distance on pass completion

The data from 2010 Brazil Serie A provides data on passing that has not been previously available. The first rule of good analysis is that when you get new data, the first thing to do is to look at the data. So I started by generating a historgram of distance of all 118,445 passes in the data set. The result (below) is much as expected with the mean distance of pass being 21.6 yards, with more passes below 20 yards than above 30 yards.

Click here for the rest of the article.

Thursday, March 17, 2011

Who's Your Analyst?

Teams, like all organizations, can easily become a group of silos. Each group is so focused on their tasks that is very difficult to make time to interact with other groups. This is usually not a good structure because one hand then rarely knows what the other is doing and instead of one team focused on complementing each other, the groups become independent actors focused only on doing what they do best. To put this in basketball terms, most organizations have a bunch of Allen Iversons - great at what they do, but not focused on the overall team goal. From an analytics perspective this kind of structure can lead not just to inefficient management, but poor analysis and often wasted time and money.

As teams look to expand their analytic capability, it is tempting to have a draft analyst, a pro player analyst, a game strategy analyst etc. and have each of these analysts sit under a manager in those departments. This is a initially seems like the proper solution and analyts become submerged in a functional area that they're supposed to become expert in and on a daily basis assist those that are trying to utilize and learn the information generated by analyst. This structure leads to two types of inefficiencies as mentioned above. The best organizations have, instead of putting an analyst function within a department, created a department of analytics that acts principally as a consulting group for internal clients.

Businesses that have structure themselves this way have generally found that analysts are able to communicate with each other on a more regular basis, allowing for more sharing of techniques and creative brainstorming around challenging analytic problems. Additionally, within the context of a sports team, having a central analytic consulting group allows for consistency in the language and style in which the analysis is presented.

The consistency of the message is particularly important in an organization that is trying to incorporate a type of information that it has not utilized in the past. If every analyst has their own style and manner of presenting data, than instead of one core institutional language, each function within the team will have their own language. On team where, eventually the scouts, coaches, trainers etc all have to get on the same page, having one core analytic language means that no one has to interpret between the groups.

An additional benefit of this structure is the ability to rapidly and efficiently deploy the analytic capacity within the organization. Different, and predicatable times during the season, different departments may require more analytic capability than their normal baseline needs. For example, as the draft approaches, the amateur personnel department may have a greater need for analysts than they do in the months after the draft or coaches may want extra analytic fire power if the team makes the playoffs.

To be fair though, most sports teams have yet to reach the point that this is an issue. The analytic capabilities are at this point mostly one or two individuals who do often work as an internal consulting group. But as teams begin to invest more in their capabilities in this area, these issues become inevitable and must be carefully thought through. Happily for those starting up now though, much of the research in this area has been done and the results are clear.

Monday, March 14, 2011

Rethinking the NFL

The current labor dispute in the NFL will be negotiated and litigated over the coming months. At its core though it is not just players against owners, but rather a three way negotiation featuring the players, against the small market owners, against the large market owners, against the players.

This of course is not an original insight, but a point that is important to keep in mind as we think about what the NFL could look like. If the litigation all actually goes to judges and juries for final decisions and the appeals are all exhausted, one possible, out come, is that the NFL is declared, by law, to be acting as an illegal monopoly in violation of anti-trust law. If this occurs, then what have become standard labor practices, such as the draft and the salary cap, will essentially be banished forever. It is worth considering what the NFL becomes in that situation, if for no other purpose, than for all parties to understand what is at stake so they can get back to the bargaining table and put a new CBA together.

Once all 32 NFL teams are prohibited from working together to set the labor market for professional football players, there will be teams who go high (imagine Jerry Jones with no salary cap) and some will go low (imagine Mike Brown with no salary floor). Economic theory and common sense both actually agree on the outcome here - a league with a few very good teams that dominate competition and  a few awful teams that do not even have a reasonable chance to win 4 or 5 games a season.

One this situation is reality, then the league will either become unwatchable (how many times do you really need to see the Globetrotters beat the Generals) or they can move to a radically different league structure. There is of course a workable league structure for a league with this type of financial structure that is in practice in many parts of the world: hello relegation!

In an NFL with relegation, we could have three divisions, with the top 12 teams in the first division, the next 12 in the the second division, and whoever is left, or wants to start a team in the third division.

In a relegation/promotion league, teams can play anyone and earn points based on the quality of teams they beat (a win against a first division opponent is worth more than a win against a third division opponent). Teams that earn the most points either stay in the first division or get promoted up to the first division and teams that do not maintain a high enough point total over a season get relegated down to lower divisions. Each division can have its own playoff system so even the Bengals can have a shot at a playoff game every once in a while.

Then there is what I'll refer to here as the Ellison Effect. Imagine if anyone could just start a professional football team and start competing in the Third division. WOuld Larry Ellison (or any of the other billionaires laying about) be interested in perhaps starting a team or two or three in Las Angeles? Perhaps an extra team in Chicago? Sure they would take some losses as they started up and tried to move up the division ladder, but the financial promise of the First division would be more than enough to tempt a few wealthy business folks to give it a try.

I could not begin to put a probability on this scenario, but it is one that is intriguing to me, because I think the drama of teams starting up and recruiting players as well as seeing teams compete to keep their place in the higher divisions would be exhilarating.

From the perspective of most of the owners though this is probably a less than enticing scenario. They have grown to enjoy their multi billion dollar TV contracts and packed stadiums. The prospect of  having to hope to fill a 20,000 seat stadium in the third division (I'm looking at you Buffalo) is probably not something that too many owners really want. Players too, at least as a collective, probably don't love the idea that half of the high paying (and high minimum contract) jobs could be gone. So with that incentive, I invite the various groups of owners and players to continue their negotiations, and not make the judges decide the future of the NFL.

Saturday, March 12, 2011

Analytics and Communication

In his excellent book on the research around the theory of Deliberate Practice, Geoff Colvin distills volumes of often complicated academic research into a clear understandable prose that is easily accessible to a wide audience. Colvin uses a series of carefully chosen anecdotes to explain the various dimensions of this complicated theory (yes, there is more to it than 10,000 hours by the way). What is remarkable about the book, is that it accurately reflects the messages and strength of the research that has been done. There  are varying levels of evidence from the research for different aspects of the theory, and those points are clearly made. The goal of this post is not to sell more copies of Talent is Overrated (though I have provided a link in case you are interested), but rather to make the point that clearly communicating research is at least as important as the work itself. This is just as true for statistical analysis as it is for scholarly research.

At the 2010 Sloan Sports Analytics Conference, I was sitting next to a high level NBA executive at a research presentation. The work being presented was interesting if not revolutionary. When the presentation was over, I went to the front of the room to ask a few questions of the presenter, but was beaten to the punch by the exec I was sitting with who said  (and I paraphrase here a bit) "oh my God, you can talk". And it was true, the presenter had distilled some very complicated analysis down to the core message and accurately conveyed the strengths, potential, and limits of the work in such a way that audience could clearly understand it. If the presenter had not been able to do communicate his research to a non-geek audience (ok, it was SSAC 2010 non-geek is overstating) he would not have been speaking to a full room by the end of his presentation, never mind having extended conversations with NBA execs about it.

Communicating statistical analysis is a careful balance between the strength and limits of the analysis. The story of the analysis has to be conveyed in such a way that a non-geek user of the information can use the analysis properly. Your projection may show that a player is going to improve their rebounding by 20% over the next 3 years, but you also need to convey the risk associated with that analysis. What are the range of likely outcomes? What are the risks?

It is tempting when when working with team executives to make it all too simple and speak in absolutes, especially when others are making similar statements about their point of view. It is incumbent on the analyst not to fall into this trap though, because our analysis does contain variance and we will be wrong. When we are wrong, it becomes easy to dismiss the analysis if we have spoken in absolutes, while if we have strongly communicated (and accepted ourselves) what are research actually says, then, while we may not win every argument, we will win more over the long term.

It is also possible to believe that we are so clever in the techniques we have used to solve a problem, we lose sight of the problem we were trying to solve. It is rare that you will run into an exec who understands or truly cares about how cutting edge the techniques are. They want to know that they are getting good information that they can have confidence in, not that you used some slick new neural network algorithm in R to get the slickest results. This is one of the reasons the communication piece can be so tricky. We have confidence in our results because of the techniques used, and while you may want to have the one sentence description of what you actually did ready in case they ask, management will only have confidence by seeing the results.

After spending hours and hours carefully constructing your analysis, be sure to put a significant amount of time into deciding how to present it. Think like your audience, and what will help them use the analysis properly. If you don't communicate the analysis effectively, then the analysis will be wasted.

Wednesday, March 9, 2011

Players: One Definition

Ask a business man to define a customer, their definition will depend largely on their function within the business. If they are in sales, they will talk about points of contact and sales histories, but if they are in product development, they will talk about demand for new features and usability. Finance, marketing, and H&R would also likely have different definitions for a customer. It is not hard to see however, how a business could benefit from having one comprehensive definition of what a customer is, that brings all of that information together in one place. R&D could then look at sales records to see if the new features under development match the needs and wants of the most profitable customers.

A sports team is no different. Every function within the sport side of a professional team has their own definition for a player. Coaches are often focused on current performance, while the personnel side is often focused on scouting information, and trainers are often focused on health related information. As each of these groups has interest in the information that the other groups have, that is not their focus, and it is often difficult for the various types of information to be synthesized.
Coaches collect, process, and analyze a wide variety of information. Game data includes quantitative data such as performance metrics related to play calls, video, and qualitative grades. Practice data often includes video, specific measurements, and observations. Classroom information includes information on preparedness, and the ability to understand and process game plans.

The personnel operation collects information from a variety of sources. Intelligence is generally qualitative information on a player's background and any current personal issues. Personality information may include quantitative or qualitative psychological assessments as well as anecdotal information gathered from other layers/coaches/friends. Specific skill data can be quantitative or qualitative information on a player's strengths and weaknesses.

The trainers and medical staff focus on a rich set of qualitative and quantitative information regarding a player's injury history including type, treatment, and recovery times. THey often use information on how players train and the frequency with which they train, as well as their pre and post training routines. They also monitor nutrition and hydration.

These are all of course gross simplifications of the broad classes of information utilized by different functions on a team. What is important is not that specific types of information, but rather the synergistic value of keeping one definition of a player that is updated and analyzed by all functions within an organization.

Once all the types of raw data and information on a player are collected consistently collected in one place, then the coach who is wondering how to better motivate a player can see from intelligence information what other coaches have done in the past, or the general manager who is wondering why a high potential prospect is not developing can see that they are not processing information in the classroom well and has poor post-training habits.

While all of this already occurs, it usually occurs when a decision maker in one function asks for information from another function. This may take 5 minutes for a response, it may take 3 days - by which time the original thought that led to the request is gone. An organization that has one definition of a player, has a system that allows thorough and creative analysis to flow freely and not be constrained by the response time of other members of the organization.

The bottom line is that having one definition of what a player is, focuses an organization on what it believes is most important about a player, and drives the resources, strategic thinking, and tactical analysis of the organization through that definition. That process gives the organizations long term strategy the best probability to succeed.

Tuesday, March 8, 2011

Why Sports Analytics?

The "Why" of sports analytics is actually fairly obvious: good sports analytics can help a team win more frequently and more consistently. The real question with sports analytics is not "why?", but "how?" By how I do not mean how do we set up a good sports analytics program - this is a vital question, but not what I am tackling today. Before you determine how to implement a sports analytics program, you first have to understand how sports analytics can help your team win.

Sports analytics can help a team win more frequently and consistently through three general functions: New Information, Synthesis of Information, and Efficient Information. Each team will emphasize a different function of sports analytics based on their core competencies, but a comprehensive sports analytics program will involve all three.
Functions of Sports Analytics
 New Information
The classic use of sports analytics is to provide new information - usually through statistical analysis of performance or game data. This function uses raw, quantitative data to create new information. Team's in all sports have done this privately, creating metrics and projections that help them better measure current and future performance of their players as well as how to best utilize the players they currently have. The power of new information is obvious when compared to the investment community. Investors that have information that other investors do not about the future earning power of a company have a competitive advantage, this why there are laws governing insider trading (hello Martha Stewart).

New Information & the Draft
In sports, if the draft is akin to acquiring an asset in the investment market, than knowing more than about the future potential of the draft eligible players puts your team at a competitive advantage, and more likely to out perform your draft slot - that is you are more likely to draft a player who will perform above the average player selected at that position. One example of this is Chase Budinger and the Houston Rockets. The Rockets are know to have the most advanced analytics program in the NBA and in the 2009 draft they put that program to use. The Rockets traded a future 2nd round pick to the Detroit Pistons for the 44th pick in the draft and selected Chase Budinger. In less than two seasons, Chase has logged over 2700 minutes, hits 35% of his 3s, and has become a regular starter. For an idea on how significantly Chase has out performed the 44th pick, previous players drafted 44th overall include: Reyshawn Terry (yes, that Reyshawn Terry), Tim Pickett, and Lonny Baxter. Houston used their analytic draft system based on college data to provide them with better information on Chase's probable impact on the NBA.

Synthesis of Information
Sports teams, like all organizations, are filled with a many different types of information. There are vasts amounts of quantitative data such as player and team performance metrics, qualitative information such as scouting reports, and hyrbid information such as medical reports. Typically these all reside in various hard drives, spreadsheets, disjoint databases, and word documents. Synergies between the various types of data often exist. That is, putting financial information together with performance data does more than just bring to two types of information together, it creates a new type of information on the value of the player. Good analytic systems bring these various types of information together not just to house them in one place, but to combine them and extract all of the synergistic value out of them.

Efficient Information
Top decision makers with pro sports teams may actually be the busiest people on earth. They are constantly being bombarded with information and requests for their time. Agents, players, fans, owners, and other related parties are always looking to get some time with the top guys and each game, workout, practice, news event etc create more information for them to consider. As Dean Oliver said "stats can see all of the games" which means that decision makers, if they have the time, can consider all of the games. These executives have deep knowledge about their sport, and no one is in a better position to leverage the information that exists into a competitive edge than they are, the limiting factor on them is generally not resources or information, but time. Good information systems give them back some of their time, by creating a more efficient process for them to interact with the information. If an efficient analytic system can easily save a decision maker 10 to 20 minutes a day, that translates into 5 to 10 extra hours a month. That is extra time to spend watching more film, talking through more trade options, exploring additional strategies, and processing even more information. These decision makers will gain a competitive advantage by being able to accomplish more than their adversaries in the same amount of time.

New, better, and efficient information are paths by which a careful investment in sports can yield more wins. It has been estimated for non-sports teams, that an investment in an analytic system with a statistical component is approximately 145% (Davenport & Harris, 2006). While there is no comparable estimate for sports teams, it is not hard to see how analytic systems can help create more wins and keep winning teams on the winning path.

Saturday, March 5, 2011

Advice for the Aspiring Sports Analytic Professional

After two very interesting days at the Sloan Sports Analytics Conference I am more convinced than ever that the most significant improvement teams can make, the area in which they can gain a huge competitive advantage, is in data management. Lets be clear, data management is not exciting. Creating great information systems is not going to get you in the draft room arguing about who to take with the 4th pick in the draft. It is however the skill that is most likely to get an analytic minded person employed with a sports franchise.

Students often ask what they can do to get a job with a team, and more and more, my answer is to acquire good data management skills. Front offices do not lack for people offering them the next great statistic that will tell them who they should be adding to their team. Some of these metrics may even be useful, but teams already have plenty of people telling them, so they probably won't know if yours is actually informative, and they are already bombarded with so much information, thinking about integrating one more type of information is painful to think about.

And that is exactly why data management is the key to the kingdom. Right now sports teams have more data than they know what to do with. The data comes in many forms, from scouting reports, contract parameters, performance data, practice data, injury reports, the list goes on and on... The problem teams have is not that they don't have enough data to consider, but that they don't have a mechanism to efficiently consider all of the data that they do have.

For most of the teams that I have spoken with, data sits in a variety of places. This may include a centralized database that has even a good percentage of the team's data in it, but there are still multiple data silos within the organization. They have their geek box who keeps reams of useful data on excel spreadsheets, they have a trainer who has a ton of health/training related data on their computer, a psychologist who has a host of data (both quantitative and qualitative) on their computer, the cap manager may have their detailed league wide cap model on their computer, and none of this data is collected and processed together. For a decision maker to truly consider all of the different sources of data that they ALREADY USE, even if they have access to it all from their computer, would likely have to have seven or eight windows open, and then scroll from window to window trying to connect the dots between the various data sources. This is a major time waster for people that place a premium on time.

Good data management which includes data structuring, processing, and front end information systems could easily save a group of top level decision makers for a sports team multiple hours a day.  Any analytical professional that can demonstrate to a team that they can pull all the various pieces of information within an organization together, and turn it into information that a decision maker can access from one open window, or one app on their phone, would enable that decision maker to spend more time actually strategizing,  processing information, and making the team better, instead of having to find and synthesize the data.

So all of you brilliant students desperate to work in sports, give up on finding the next PER or adjusted +/-, and get the skills necessary to help give decision makers back some of what they value the most: time.

Thursday, March 3, 2011

Get Your Geek Out of the Box

Organizations that have taken use statistical analysis often do so with a “toe in the water” approach. They hire a well regarded blogger to do a project to see if the information might prove useful. This usually takes the form of some sort of player projection system. The front office then huddles around the results and decide if the analysis points out anything useful to them. If so, they may even bring the analyst in on a full time basis to provide them with information. The analyst then gets a desk in the team offices and, equipped with a high powered computer and the newest version of Excel (and maybe R or Stata) they get to work mining data for useful insights.

This is often the end of the story though. The analyst works diligently and adds value to the organization, providing useful information and everyone is happy. But the organization is not maximizing the value of the analyst, because all the useful insights into player value and game strategy that the analyst may bring to the table are put into a special geek box. All of the other coaches and personnel folks open the box on occasion, look at the information, and even utilize, but then they put it back in the box and do not see it again until the next time they choose to open the geek box.

This leads to a situation in which, when the non analyst wants some information, they either have to go find the geek box or find an alternative geek box. It is often, particularly for coaches out on the road, easier to find information that looks like the information from the geek box online from sources like ESPN or HoopData. So when a coach wants to know something about their opponent, they may turn to alternative sources – outside of the organization. 

The reason the geek box is a problem is that it creates multiple versions of the truth within the organization. Consider the situation in which an analyst has, through careful analysis, developed a metric that isolated a RBs contribution from their offensive line’s efforts. Occasionally the general manager opens the geek box and sees that, while his team has a strong running attack, their RB ranks near the bottom of the advanced metric. The head coach has been too busy to open the geek box and instead saw quickly on ESPN that his RB is in the top five in the league in yards per rush. There are now two very different impressions of the team’s RB floating around. Now, instead of the GM and coach meeting to discuss how to solve their RB problem, they are headed for conflict as to whether to offer a big extension to the RB or not.

The solution to the geek box problem is an enterprise wide approach to analytics. This is the opposite of the toe in the water strategy, it is the canon ball off the high dive approach. It puts all of the various forms of information that sit within the organization – scouting, statistical, medical etc in one place, integrates it all, for anyone within the organization to easily access and utilize. Now, when a scout is on the road and wants to have a more complete view of the player they are about watch, they instantly have the all of the relevant information on the player, and it is the same information that the GM and coach are looking at, so when the three individuals are doing their analysis, they are starting from the same place.

Making information more accessible to all, and keeping everyone on the same page has significant benefits to the organization. My bias is of course to point out the benefit of having the statistical information easily available to all, but the truth is it goes both ways. As analyst, we look to inform the rest of the organization, but we can also learn a great deal from seeing the information that everyone else in the organization is looking at, so when we have conversations, we are starting from the same place as well.

It is time to take the geek out of the box and have a full enterprise wide approach to information in sports organizations.

NFLPA's Secret Leverage

Judge Doty’s recent decision to relieve the NFL owners of their $4billion extortion fee from the networks garnered the predictable reaction from the NFL that the decision would have no impact on the ongoing labor negotiations. That reaction is obviously as phony as a tight rope walker suggesting that removing his net would have no impact on his decision to have a shot and a beer before attempting a high wire walk.  The question is not whether this decision would have an impact on negotiations, but rather how significant the impact will be. 

Mark May of ESPN was recently quoted by Wilbon on PTI that while the owners would certainly like to have the cash, their pockets are so deep that it really does not affect their calculus on the labor front dramatically. This comment is born of May’s personal experience as a player negotiating against many of these owners. May’s experience though was at a different time, and the NFL and the US economy have gone through some major transformations since the last time there was a work stoppage in the NFL.

During the first six or seven years of the last decade, while the NFL continued to solidify its position as the most dominant for in professional sports, there was a burst of economic growth and activity across virtually every industry in the US. CEO’s across the country were pushing to realize higher and higher rates of return for their investors and many leveraged their assets to produce those returns. The debt that many of these companies took on finally proved to be too extensive as the economic crisis of 2008 hit. Many previously very successful business were forced either into bankruptcy or to sell out at pennies on the dollar to rivals.

NFL owners were operating in the same economic world as Washington Mutual, General Motors, and hundreds of small businesses that no longer exist. It occurs to me that it may be a bit naïve to believe that none of the NFL owners got themselves over extended and perhaps are carrying far more debt than their fellow owners realize. If any owners are privately facing this situation, they have been able to delay the reckoning that other businesses have gone through because the money tree that is NFL ownership has continued to flower during the economic downturn.

Without the $4billion from the networks though, for the first time, there may be some owners out there that are looking at the money tree and wondering if getting a labor deal done quickly isn’t the best way to get the tree to flower. The alternative may be rather uncomfortable for some of them.

Wednesday, March 2, 2011

Sports Analytics is Not a Strategy

As I travel towards the Sloan Sports Analytics Conference I  am pondering what sports analtyics actually is. Part of that thinking is spurred by the book Competing on Analytics by Davenport and Harris. One of the first points that Davenport and Harris make is that analytics is not a strategy in and of itself, but rather a tool that should be used to support the "core competency" of an organization. This point is, I believe, one of the core misunderstandings of what it means for a team to start using analytics. There seems to be an impression that statistical analysis (one tool of sports analytics) is a strategy that dictates a certain style of play and/or a certain type of player.

The legendary book Moneyball by Michael Lewis served sports analytics extremely well in detailing how a team that competes with analytics can prosper. The book also, however, reinforced the impression that analytics had a unique purpose - unearth undervalued players and find areas of in

game strategy for which the excepted wisdom was not entirely correct. While this is certainly a viable use of analytics, it is hardly the unique or, for some teams, even the most productive use. The A's employed analytics in this manner because it fit with their core competency: winning with a low payroll. It was the strategic goal of the organization that determined how to best use analytics, instead of analytics dictating a strategy to the team.

Teams have their own unique core competencies and areas in which they choose to have as strengths. In the corporate world, Walmart competes on price so they utilize analytics to better manage their inventory. In the NFL, the Ravens have traditionally competed on elite level defense and evaluation of collegiate talent. Should the Ravens choose to use analytics to help provide a competetive edge, then their initial focus should principally be in support of these functions, which is fundamentally different from how the Saints or Colts might choose to best deploy their analytic assets.

At this stage in the growth of sports analytics, it is incumbent on the analysts to communicate to team executives, that analytics is not about identifying the "best" strategy or players, but rather to work within the core competency of the team and help push that forward. For the most part however, we as analysts have done little to demonstrate the value of analytics beyond identifying new metrics to rank teams and/or players. While I am certainly guilty of this, and to a large degree it has been necessary, I believe that we need to start pushing the discipline beyond this and demonstrate to teams how analytics can be used to help make better decisions, develop a culture of evidence and uniquely support the core competency of each team.

Analysts working for teams that place a high value on their player development function should have a very different focus than one that focuses on finding value in free agent markets. Having had the privilege to work with a variety of talented executives, every analyst should understand that these leaders understand their sport at a truly elite level and have developed long term strategies that they believe in. When an analyst can demonstrate how a strong analytics program can function as a tool to insure that the tactics used to support those strategies are the ones with the highest probability of success, then they will maximize their value to the team.